> There's something incredibly peaceful about being in the hands of an expert you trust. [...] AI can absolutely shatter that feeling in an uncomfortable way [...] but I don't know if I can fully trust AI either.
This really is key. We know we can't trust the AI, but at the same time we're also more comfortable asking the AI for clarifications or confronting it. Not having a time-bound appointment or paying by the hour helps a lot. But even then, more information doesn't necessarily help!
I once brought my 11-year-old car, a Civic with 150k miles, to multiple garages. I figured I'd play the "second opinion" game to correlate what the garages recommended to decide on what needed to be done...
I got 3 completely unrelated recommendations, including one that I knew was invalid! I felt worse off than when I started!
The solution to uncertain information isn't more information, which the AI can certainly provide, it's better information, and AI cannot currently provide that.
I have multiple LLM subscriptions at any given time, plus an array of local models.
When I ask a question outside of my domain of expertise I like to ask all of the LLMs I have access to. I also create separate sessions and ask the same question multiple ways.
It’s revealing to see how many different and contradictory answers I get, most of which are presented confidently.
The last time I ran a medical question through Claude I couldn’t even get consistent answers between sessions.
It’s also scary how easily you can lead each LLM to the answer you have in mind. When I would start asking questions about different options that other LLMs had presented, each session would drift toward that explanation.
In my day job we tried creating a credit assessor tool using LLM as the credit assessor.
It did great, generated a report on the assessed business that was incredibly detailed and plausible.
Then I started running tests and getting into the details, and found that if you ran the same report on the same data, it generated completely different, still very plausible, results. I could run the same source data through the assessment process 10 times and get 10 very different results. We had to can the project and go a different route.
LLMs are designed to produce plausible results, not factual results. We can fix this when using them for software dev by using linters and tests (though we've all had the experience where the LLM invents an API endpoint). I would not trust raw LLM output in any situation where that kind of testing and verification capability isn't present.
The problem is how do you know whether the answer is just the most persuasive or actually the most accurate one? It's hard to figure this out without domain knowledge.
I do something similar with reviewing code: I have one agent write the code and another reviews it, then they go back and forth for a bit improving the code. Seems to yield better results than one agent alone.
The difference is that in the code situation, you can run unit tests on the code, compile it, etc. Unless your LLMs are ordering diagnostics and reviewing the results, there is no further information that the LLMs have on the situation. Having a second LLM review the first is counterproductive, if the 2nd LLM is better, why not use it directly? If not, then what prevents it from sending the first on some incorrect tangent?
Worse is that LLMs are trained to be persuasive by default. The "you're absolutely right..." stereotype is because these things are A/B tested on response quality and we know from studies people reliably rate vibes better then anything else - e.g. while the quality of hospital accomodations likely has some impact on patient outcomes, the view and decor of the room certainly did not fundamentally change the quality of the care provided but it is the largest determinant in how well people rate that care.
There's a big difference between a _puzzle_ and a _mystery_. In a puzzle, the goal state is known, and as more pieces - data - appears, the goal gets closer. You know how far you are from the goal.
A mystery is worse. With each additional piece of data, the goal gets farther away. Everything is more and more confusing.
Maybe I am missing something but I just find this wrong.
Everything is a puzzle: there is one "Truth" or one diagnosis. You (a smart human) should be able to converge on it by cross-examining your LLMs. By themselves, they have no interest in revealing this, no stakes, which makes them tools only useful at the hands of a capable investigator.
> You (a smart human) should be able to converge on it by cross-examining your LLMs.
What makes you think this is fundamentally different from cross-examining ELIZA? There is no guarantee that the LLM will help you converge on anything. Indeed actually calling out an LLM on BS tends to eventually produce an "I don't know and can't help you further" answer (as it should).
> There is no guarantee that the LLM will help you converge on anything.
Absolutely. The guarantee does not come from the LLM. The LLM is a simply an improved version of Google Search.
The guarantee can only come from a systemic application of epistemic discipline and reasoning, which is very much (smart) human territory.
Put it another way, I could make good decisions with/without LLMs, with some uncertain diagnostics as input. I would have to trawl through 50 papers myself, and it is possible that my decision arrives 5 years too late as a result. LLMs enable trawling and do some of the legwork in connecting the dots, but are ultimately only as capable as the orchestrating human.
The problem is that the diagnosis might not be known for a while. There's a few conditions and diseases that require an autopsy for a guaranteed diagnosis and therefore are diagnosis based on symptoms in clinical settings.
> The solution to uncertain information isn't more information, which the AI can certainly provide, it's better information, and AI cannot currently provide that.
I'd argue that AI _can_ currently provide that, but that it can't do it _reliably_, and that to non-experts it's impossible to differentiate, which makes it all the more dangerous.
Isn't that the case with human "experts"? If you had encounters with doctors, mechanics, etc. you'll know you can get a completely different diagnosis for the same problem which obviously means (in most cases) that the person you thought an expert is wrong.
What is needed are studies that will take a cold look at the actual results because AI seems to be required to be perfect or it is useless. It just needs to be as good as a human for most stuff, but in the long run it will be much better. At least that what extrapolating current reality shows us.
We have systems around humans that exist to manage expertise gaps, credibility signals, and accountability. This is part of what makes humans as good as they are, along with specialized training and some measure of meritocratic selection. We license and regulate and account and litigate to make a system that responds and improves.
Some of this might be applicable to LLMs, but some isn’t and much of it would be resisted. This is one reason we’re not likely to get “as good as a human” because at some level we’re not optimizing for the outcomes; we’re optimizing for speed, convenience, some participant’s economics, and underlying beliefs.
I've been going through PT for a hypermobility disorder related injury and I've use an AI to help me figure out "interview questions" to see if a PT knows anything about hypermobility or is willing to learn. I found it helpful to select a new PT after my first PT I trusted made things worse by prescribing stretches and no load progression from rest and recovery back to deadlifts
People put a lot of faith in human “guardrails”, standards, etc. But the same argument could be made that trusting human experts without discernment is as dangerous as trusting AI or Google or whatever other non-human source. It’s always been the case.
YouTube has saved me at least that much in appliance repairs... and it doesn't even have an AI. It's amazing how valuable access to information can be.
Tbh LLMs pulling data out of medical documents in it's training set and searchable online is likely a much easier task than fixing some weird CSS alignment issue.
A few years ago (before the AI craze), I was misdiagnosed with tuberculosis. I had a chronic cough, and an outsourced radiologist at a clinic found signs of tuberculosis. The findings were sent to the city's tuberculosis hospital, as required by the country's law. The doctors there took the radiologist's conclusion at face value and required me to stay at their hospital for at least 8 months under a strict, prison-like regime. There was no option to say no, because I was considered some kind of biohazard, and by law I had to comply.
Before I was admitted, I quickly found another radiologist, who diagnosed pneumonia instead. I sent his report to the chief doctor at the tuberculosis hospital, and after some deliberation they concluded that the original reading was wrong. Turns out the doctors there can't read scans at all and just believe whatever a radiologist says...
The funny thing is, they had already officially put me on the tuberculosis register and didn't want to admit they had made a mistake. So instead, they simply gave me another paper saying that I had been cured of tuberculosis by them... in 7 days. I'm probably the only person in the country to defeat tuberculosis in a week :)
So if you don't trust the radiologist/doctor, maybe find another doctor if you can afford it? You can compare their conclusions and see if they match. Two unrelated doctors or radiologists saying the same thing is probably about as close to the truth as you're going to get. I'm not sure though whether I should trust AI or humans more. AI can hallucinate, but I've been misdiagnosed by humans so many times too...
Yeah, I know! It was strange. They gave me a test, and it came back negative, but they insisted it was negative because I had "latent tuberculosis," which supposedly wasn't detectable by the test yet but was about to become active.
I forgot to mention that, besides getting a second opinion from another radiologist, I also took a more modern test at another private clinic. That test has better detection rates than the one the state clinic used, and it came back negative too.
I have suspicions they had some kind of government quota to keep the hospital staffed with patients in order to receive funding. Or they were just completely incompetent. I pushed back by bringing them another radiologist's report and the results of a better test that I paid for myself, so I guess they decided to back down.
I had a similar experience. My son had pneumonia and was still filling pain after 10 days of antibiotics. Took an X-Ray to three different doctors, and only one got the right diagnosis (pleural effusion). It's really something we should have a central place with top notch professionals looking at it, instead having each doctor to find by themselves.
Radiologist. I don’t read MR shoulder exams in my day to day practice, but from the few pictures shown , I can’t conclusively disagree with the original report.
These models are generally terrible at reading medical images. The amount of public training data on the internet compared to the number of scans a radiologist reads in training is minuscule. There’s obviously a ton of medical images in general but very few, and even fewer along with a report are available on the internet publicly for download.
There are vision language models coming out of research labs that are excellent in describing and localizing findings. Still at the level of a 1st or 2nd year radiology resident, but as we all say - this is the worst the models will ever be.
I'm a radiologist but can't really weigh in without seeing the full 3D MRI dataset. Regarding this point:
> They performed shockwave therapy on my shoulder even though a recent clinical practice guideline says clinicians should not use or recommend shockwave therapy for rotator-cuff tendinopathy without calcification; I was told during ultrasound that there was no calcification.
Ultrasound isn't a great way to assess for calcification. It'll find large calcification but easily miss small ones. Plain radiograph would be more helpful, but the MRI may have revealed it as well. Either way, shockwave therapy isn't harmful in the absence of calcification--it's just not helpful.
Edit: when a radiology report says something isn't present, there's always an implicit caveat that the finding isn't present within the context of the modality and images obtained. So an ultrasound report can state there are no calcifications while a plain radiograph can report the presence of calcifications without being inconsistent. Obviously very confusing to patients and people unfamiliar with medical jargon, but clarifying this in reports would make them sound even more qualified, "hedgey", and annoying to read than they already are.
> So an ultrasound report can state there are no calcifications while a plain radiograph can report the presence of calcifications without being inconsistent. Obviously very confusing to patients and people unfamiliar with medical jargon
This is being overly nice, I think. Anyone who doesn't understand this is an idiot imo. You would have to assume that every type of diagnosis instrument has infinite clarity and is always correct to be confused in this case.
Reminds me of the Babbage quote where somebody asked him, if I put the wrong question into this computing device, will it still give me the right answer? His response, paraphrased "I can not fathom the logic of the minds which would come up with such a question".
> Anyone who doesn't understand this is an idiot imo
I don’t think that’s true. Avoiding this mistake requires knowing that an ultrasound may not detect calcification. For a patient reading their own report, I don’t think that’s intuitive. I would expect most people to read “no calcifications” and assume that their joint has no calcifications.
Most people should have learned at a young age that absence of evidence is not evidence of absence. My 8 year old understands this. After all, you can rarely ever prove something does not exist, only that it is unlikely to exist.
If a report states that X was not found, it does not mean X did not exist, it means it was not found.
What may be lost on the layperson is the nuance and understanding of how thorough or not a particular scan is and how much weight to give the findings and thus the odds that the report is correct.
This is - by far - the most stupid stuff I've read on the internet the past few days. They didnt find cancer either (as well as a plethora of diseases that could be related to the symptoms), and afaik its not in the report.
Yah you can argue that the tool is not ideal for that diagnostic, yadda yadda. I get it, and in the end I agree with the subtle difference you highlight, because it is something that makes sense to a certain kind of people. You know how many medics would read the report exactly like the author did? Too many.
How do I know? Im not in a wheelchair after being constantly misdiagnosed by using the wrong imagiology technique by (mostly) chance, and a good help from friends, including a surgeon. This seems to be a case where AI would be a valuable doctor tool for differential diagnosis; instead we have know-it-alls that can't bother to verify, and AI that often gets details wrong. That is the problem.
I think it’s the combined depth AND breadth of knowledge that can be captured by AI models that is going to make them way better than most humans at this kind of stuff.
It's like when finding out about the sex of your baby via ultrasound before they're born. If you're told it's a boy, you can be pretty certain you're getting a boy. If you're told it's a girl, you shouldn't get too attached to the idea. The ultrasound tech might just have missed the evidence your baby was a boy.
> You would have to assume that every type of diagnosis instrument has infinite clarity and is always correct to be confused in this case.
There's a difference between 99.9% clarity and 50% clarity. Even if neither exactly equals 100%, it's understandable that a layperson would expect different language between them
I don’t think people are idiots if they don’t understand how a normally intelligent person might not intuit that. I do think they have a seriously underdeveloped theory of mind.
"On two occasions I have been asked [by members of Parliament], 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question."
Off topic but I have always felt this seemed like his misunderstanding rather than theirs. It’s an odd question, but it’s a very sensible point to make if Babbage has just told you this will solve the problem of mistakes in calculations - humans being involved at the start means human error still plagues the output.
> I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.
Well, he did diagnose the situation correctly. He couldn't comprehend the confusion of ideas that provoked the question.
I'm also not entirely sure it's an odd question to ask. To this day, users are surprised when their software produces garbage output instead of failing. Perhaps the members of parliament were expecting some form of input validation or sanity checking out output.
Looking into his biography, it seems that he was indeed pitching the engine not as a means of efficiency, but as a means of avoiding mistakes in mathematical tables. It would have done Babbage well to insist he couldn't possibly solve all classes of mistakes, but would have solved a great many of them! "Why yes Senator, you are quite intelligent and handsome and make a fair point, allow me to give you the finer picture..."
Would have also been a fair point if Babbage had channeled his inner techbro and insisted it would directly replace human calculators; simple machines like Babbage's will chug along blindly on obviously erroneous data, but humans for all their sloppiness can often backtrack on errors.
> Anyone who doesn't understand this is an idiot imo
I disagree. A priori it's not obvious to a layperson whether or not a statement that uses unconditional phrasing is intended to be authoritative or conditional on something unspecified, like the resolution of the measuring device. This goes for any sufficiently technical field.
If you got the brakes checked on your car, and the mechanic did <something> and told you there are no issues with them, and you then took your car to a different mechanic who did <something else> and told you there is a problem, you would not be an idiot for thinking that these conclusions contradict one another.
As a rad tech, YOU TELL ‘EM DOC! I do like some uses of AI I’ve seen that help patients advocate for themselves or understand basic things like blood panel numbers, but it’s really bad at glazing people and leading them down medical rabbit holes kind of like the OP.
You would think that the AI would point out that calcium is best demonstrated on Radiographs/CT imaging vs Ultrasound or something to that effect.
Semi-related: my father has complications from a motorcycle accident ~25y ago that crushed arteries in his leg coupled with diabetes (insulin / kept sugar at ~100 and his A1C was kept under 6.7 for ~15y). 6w ago had to have his toes removed due to dry gangrene; they eventually (2.5w ago) had to remove his leg below the knee because of the severe blood flow issues below the knee.
Between the toes and the below the knee amputation, there were no less than 15 different doctors and PAs / related personnel who COULD NOT COME TO A CONSENSUS. They would just tell my mother and I (PoA) the details; they refused to come up with a singular plan of action moving forward, leaving it up to us to make 'an informed decision,' something that's IMPOSSIBLE when you have to take up to 15 different opinions into consideration.
What exactly are we supposed to do as patients/family members when medical personnel cannot give reasonable paths forward and instead just throw a bunch of shit over the fence at you and tell you, "you decide what to do from here," regardless of how many VERY DIRECT conversations I had w/the 'care team' on doing better to provide a limited array of options and reasons/likelihood of 'positive outcomes'.
I'm used to dealing with a wide variety of stakeholders/SMEs in decision-making; it's my job to apply my extensive industry experience to present our clients with their options, ranked and reasoned. Doctors, in my experience and most recently with my father, clearly do NOT do that (I assume due to liability; but, no real idea, honestly). So; when dealing with LIFE CHANGING circumstances, what are we supposed to do except rely on what might be able to offer more analysis and option narrowing w/AI?
I certainly don't want to make the job of medical staff more difficult by putting out crazy theories I found on the interwebbernets through my own research, etc; but, when we're having to deal with uncertainty and insanity, what else can we do?
This lines up with my experience with my mother, though it played out differently. In her case, she would switch doctors every ~5-10 years and each time they'd basically say everything the previous doctor said was wrong. First it was "you have Lupus", second it was "actually it's some other autoimmune disease", then it was "actually whatever you had has been in remission for some time now and you've been taking brain-numming medicine for no reason." Then it was "you have cancer", "it's a rare one", and "oh turns out the brain-numming meds have a correlation with rare cancers". The cancer part was handled well (albeit unsuccessful) though. After such a bad time with rheumatologists, I was shocked by how competent people were when it came to cancer.
All of the above was intertwined with brief stints with doctors that would just berate her for being a painkiller junkie, even though she hated the stuff and just wanted to find/fix the problem.
Kind of a rant, really. I'm not sure how to tie it back into AI. I do wish we had AI at the time so that we could at least cross-check, but I also understand that doctors are already sick of patients self-diagnosing on the web and that AI probably just makes that worse. At the same time, if our medical system could catch up a bit (more doctors? less corruption/paperwork? not sure what it needs) then maybe people would be less inclined to take matters into their own hands.
I'm sorry to hear that. The accusations of drug seeking are particularly galling.
AI is absolutely a god send for patients navigating the medical system.
I know the US system is horrible and I sympathize with doctors doing their best within it. But we must admit, they are also responsible for the countless stories just like yours, and have contributed to the public's deteriorating trust of medical institutions. It's not just the insurance companies and conglomerate CEOs.
Probably liability... on the amputations I indicated and contraindicated, it's increasingly difficult to navigate trough patient perceptions while not disclosing so much as to give them rope to hang us. Some decisions are a game of probability that often we don't have clear numbers. In trauma, I have both cases where I recommended an amputation and at last minute decided to see that happened and the patient is walking with their leg today; and cases where I didn't recommend and later had to amputate as the lesion evolved. With cancer it's more straightforward, the cancer is what dictates the surgery... some cancers have poor response to other treatments, so we amputate. Some cancers had invaded the neurovascular bundle, so curative options involve necessarily amputation to get good margins. In cancer there's less doubt in the prognosis, so less chance of legal ramifications.
Your see this in coding agents too. The only times so far I’ve really seen Opus tie itself into a knot is where I’ve asked it to fix something that I thought was broken but actually wasn’t in the way I had described. It will bias towards your description (I’m guessing because that’s the most recent context it has?).
I feel like the promise of these models is to help people make more informed decisions. Improving the knowledge economy and general understanding.
The problem is these are just statistical models at the end of the day, so you need to know something to be able to identify the errors. You can’t let them really be autonomous and you also can’t really have people turn into glorified approvers. If the machine is correct 89% of the time, you cannot make people responsible for that 11%. It’ll just cause automation fatigue.
tl;dr: the actual use cases of these LLM (or generative AI in general) is rather limited, so it is offensive how much hay has been given to them eating the entire capitalist system. They are not fit for purpose.
i'm sorry, but AIs only "know" about stuff that they have been trained on.
If we would allow AIs to be trained on the petabytes of medical data hidden in hospital systems, they would most likely be much better at diagnosing illnesses and conditions than the average doctor.
(Justifiable) Privacy around medical records so far prevents this.
You think you're cheering for humans, but in fact you are gatekeeping healthcare.
I dunno... if we gave an AI all of these medical records as training data, wouldn't it be trained to give the same answers as the doctors already gave, without knowing whether those diagnoses were correct or not?
Except it would see all the times similar starting conditions led to different diagnosis and recognize those contradictions. Or all the different treatments and their outcomes. And it would never forget or have bias.
It would be like the sum of all medical professors in existence.
Radiologist who does read shoulder MRI would like to add that over half the annotations are wrong, glaring mistakes in anatomy and cardinal direction which begs the question of how is it making these findings without knowing what it’s looking at (here’s a hint, it’s hallucinated based on reports it sees).
Agreed. Not a radiologist, but I do a fair bit of MRI research. Experts vs lay people probably have different success with getting the right diangosis out of a frontier model. Subtle changes in prompts can cause different diagnosis[1]
Huh, I'm reading and looking up these words you guys are saying and it is starting to look exactly like the symptoms I have been having with my own right shoulder! I feel like a giant gaping rabbit hole just opened up next to my desk.
Why isn’t diagnostic ultrasound used in orthopedics? They inspect fetus hearts and other organs everyday, why not shoulders? Seems much cheaper and faster.
They do. Ultrasound in orthopedics is a relatively newer field, and there aren't quite as many sonography techs and radiologists experienced in reading these studies, which is likely why you don't see it offered more widely.
Edit: I should mention that ultrasound is basically unusable for evaluating bones. Sound waves can't penetrate bone, and so you end up just seeing a huge black void. That's a huge orthopedics use case that ultrasound just can't benefit. However, ultrasound is fantastic for evaluating muscles, ligaments, tendons, and other superficial soft tissues.
Serious question: If the bones specifically show up as black on ultrasound but the surrounding (muscle, etc) don't, wouldn't that be an option that could be used to try to determine a broken/fractured bone without the radiation from an xray? Or are the gaps in those cases usually too small to pick up?
We order ultrasounds all the time for shoulders (for like soft tissue issues; for trauma, you'd start with an xray). For other joints, such as the knee, MRIs are a better choice (unless htere has been substantial trauma, in which case xray initially or further), though more expensive, unless you're excluding a Baker's cyst, in which case an ultrasound is fine.
Since MRIs are more expensive, private doctor's might order them instead of an ultrasounds.
Where are you? Pi and work comp attorney in medium US midwest metro. I've never seen one in 20y. Not from HCA ERs, medicaid er visits to univ affiliated er, nor prestige practices.
Ultrasound was overlooked by US medicine as a first line imaging tool for a long time because it takes real skill and experience to do it right. But it's making a comeback. We've had Chinese, Indian, Australian, and American doctors visit us for one to two month stints to build up their skills.
Given the skill involved, it's probably a liability concern they don't want the exposure over there.
It's a manual, non-standardized process without a standardized output. Image quality depends both on user skills (how deeply they press the sensor on the skin) and the machine they have. Unlike CT/MRI the examination results cannot be easily shared and compared between patients for studies.
Any comment that doesn't start with this or similar qulaification should be taken with a grain of salt (yes, including this one).
Medical imaging is one of those things everyone thinks is simple because they don't know what they don't know. I'm a cardiac sonographer, and I have to assume radiologists hear at least as many eye-rolling takes on AI coming for their job as I do.
I don't completely understand what you mean, but I can tell you for my job, having AI tell you how to get the images is (without exaggeration) like putting someone who's never played an instrument on stage and saying "don't worry, the AI will show you how to do it."
I did a lot of cardiac MR and often GA cases. Sometimes after the scan an echo would be done.
I know my anatomy and etc and have done a short stint in ultrasound. I have no idea what you are doing or looking at and can identify pretty much nothing.
Echo techs are going to be around a lot longer than MR techs.
I mean, probably not. No expert, but everytime I go to an immunology meeting (I'm a paediatrician) they've got a whole stack of new diseases. The field is moving fast, and there has to be a careful amount of shared decision making about when to test, what a positive test means and so on. I reckon they're as safe as any of us.
> Does radiology really make +$700,000.00 a year ?
The radiologist I know does not, but they are paid very well (and these numbers are always dumb when you're not sure if they're living in Manhattan vs literally anywhere in Kentucky)
Like most medicine, a large % of the job could be done by any decently talented person willing to follow instructions and shadow for a few months.
Like most medicine, the remaining % is what you're paying for, because it is literally life and death and you can't do things like "pull the logs" or "lets turn it off and take it apart" or "huh i need to put this down and come back later". Even in radiology, because "well lets just do it again to be sure" is often not a viable option.
While there is a problem in how we have inflated the cost of education for medical fields, the insane health insurance issues (US obviously, but it does have some effect globally when the expert radiologist you hire from the US to help with research costs that much), and probably some better ways to approach splitting the work for the entire field, like most professions dealing in life or death, medicine likely will always be paid well.
It funny to see the community here expects the human body to be treated like a deterministic function: for input X expect output Y - and that transfers to diagnosis - people expect to receive the same diagnosis from different specialists for the same issue.
Given human body complexity, the diagnosis is a compound output of the experience, knowledge gained throughout the career and diagnosis methods/equipment, the title (like Dr) is a certification imposed by the state so its "safe" to let people practice since they passed "the bar" - but that doesn't imply everyone will be treating the same.
Some specialists update their knowledge monthly, some yearly and some don't do it at all, there are so many variables in play here (geo, politics, even weather haha).
Having said that, choosing the specialist is really important, getting opinions about their practice and their speciality, you can only maximize your chance of getting the right diagnosis, but don't expect to get it right just because somebody is called a Dr.
I'm not sure what your point is. Are you saying that medicine is inherently fallible and therefore AI is more likely to make a good diagnosis - particularly a cluster of specialist AIs?
Yeah I think the OP is muddling the point by conflating "physician's version of the diagnosis" with "The Diagnosis".
There is absolutely one "The Diagnosis". Human body is a machine, albeit a very complex one, and all measurement sources have noise. But they are all measuring one reality, and if there is a problem, there should be one explanation that all measurements align with. They can be noisy but can never be conflicting (instrument error notwithstanding).
Physicians' ability to arrive at "The Diagnosis" would vary, but it does not mean one does not exist. I am not sure if characterizing human body as derministic or not is relevant here.
I think „the diagnosis” is over simplification and lots of professionals would disagree that there’s always a single one. As a patient your goal is to eliminate the symptoms of whatever is going on in your system. Often times there could be many reasons for it and only curing one can help you already. The diagnosis is a help tool to choose the roght curation method.
Thus, chasing the „right” diagnosis (whatever that is?) is pointless, as it only the outcome (reducing symptoms, stopping the damage) can tell you if the diagnosis was right, but not the only one right.
> I think „the diagnosis” is over simplification and lots of professionals would disagree that there’s always a single one.
"The Diagnosis" does not mean "one root cause".
Situation: my car has some unexplained vibrations.
1. Mechanic A says that it is the engine mounts
2. Mechanic B says that it is some weirdness in how the exhaust assembly is hanging to the underbody
3. Mechanic C says that it is just my wife farting
I replace engine mounts and 40% of the problem is reduced. I then drive without my wife and the remaining 60% is solved.
"The Diagnosis" was: 40% mounts, 60% wife, 0% exhaust.
This is a kind of thinking a lot of programmers fall prey to. The real world, outside of code, is a very fuzzy and inherently analog place. There is very rarely one in any complex system having a complex problem needing a complex solution. At some point even the definition of diagnosis gets fuzzy.
The best demonstration of this in medicine is probably the DSM-5. What, really, is the difference between Narcissistic Personality Disorder and Borderline Personality Disorder and Generalized Anxiety Disorder? Can they overlap? (Yes.) How do you treat them? (It's not easy.) What about depression: how do you tell if someone has Major Depressive Disorder or Bipolar Depression? (Again: not easy.) In some circumstances the only way to tell the difference between the two is what drugs work: if antidepressants help, it's Major Depression; if mood stabilizers help, it's Bipolar Depression. It's kind of odd to define a One True Diagnosis by "well we fixed it this way, so it must have been that", with no other way to do it, isn't it? (What if both work? What if one works for a while, then the other works? What if treatment with antidepressants induces bipolar (hypo)mania? All of those happen!)
Pyschiatry gets complicated because the failures are not mechanical. Even if you could image every single neuron in a person's head we do not have a very good way to define an algorithm for these issues. I do not have a good answer for psychiatry.
> This is a kind of thinking a lot of programmers fall prey to. The real world, outside of code, is a very fuzzy and inherently analog place.
Having said that, I would vehemently reject and push back against this, and without doubting your sincerity, characterize it as an ad hominem.
The vast majority of issues with the human body are mechanical in nature. Restricted blood flow, unwanted tissue, a broken bone, a bad valve etc. These are causal descriptions of "disease". Where causal descriptions exist, the "One True Diagnosis" principle holds. Psychiatry just happens to be unique in that it is a fuzzy science where we rely on checklists and ultimately all diagnosis is probabilistic.
EDIT:
> This is a kind of thinking a lot of programmers fall prey to. The real world, outside of code, is a very fuzzy and inherently analog place. There is very rarely one in any complex system having a complex problem needing a complex solution. At some point even the definition of diagnosis gets fuzzy.
I would also push back against this mindset in general. This is not a falsifiable claim, it is incoherence as an argument, and I do not need to be a programmer to hold this position.
That the real world is analog is irrelevant to its amenability to causal explanations. Or "fuzzy": "fuzzy" in this context just does not mean anything.
I am not trying to sound exasperated or win internet points, just impress this point on you and anyone reading this. We can write math to predict weather, make it tractable to solve using approximations, tolerate IEEE 754 weirdness, and finally tell what the clouds will do a week from now. This is nature telling us that there is a pattern to how it behaves, and it is the only weapon we have as scientists.
To say that nature is not amenable to explanations is a very defeatist thing to say: neither Newton nor Einstein nor any of the million-odd people that have built modern society would exist if nature did not have causal explanations. I urge you to reject this defeatist thinking.
There's quite a few diseases and conditions that don't have definitive tests. For example, alzheimer's and parkinsons are diagnosed based on medical history and symptoms. With alzheimer's an autopsy can tell for sure but that's not much help for a patient. I'm sure there's other things out there with similar situations. Hard to come up with "the one true" diagnosis with an definitive way to determine it.
> With alzheimer's an autopsy can tell for sure but that's not much help for a patient.
Ok let us unpack this statement.
For your point to hold, I would have to be saying "all kinds of practical diagnostics are invented now. No progress can be made in better diagnostics".
If Alzheimer's can be validated by slicing open a dead patient, there is a causal mechanical explanation for the disease. If we can not confirm that defect without slicing open the patient, that is a limitation of 2026 tools. The "One True Diagnosis" is an Oracle explanation that all real diagnostic techniques try to approach in the asymptotic sense, and it is helpful exactly because it clarifies in discussions like this.
There are going to be diseases where we do not yet have causal explanations. Or where we treat them without establishing them. Hypertension is one example: while technically it can be caused by vascular stiffness, some weirdness with the RAAS system, some hyperadrenergic weirdness, practically you get a lot of mileage out of just prescribing people telmisartan if they're old.
That does not mean the frontier of hypertension is settled, or the 10% who do not have a vascular stiffness problem would not benefit from better causal models of hypertension. Science is us continuously pushing back against the fog: of the tools we have in 2026, some are great, some are imperfect, some are promising etc.
There might be "one true diagnosis" but there's no reason to believe that we'll have practical diagnostic tools to get it. If we need to sample the brain chemistry to diagnose a neurochemical disorder, it's probably not too useful in a clinical setting. The world makes no guarantees that we will be able to differentiate between certain situations with tools that we can realistically access and build.
Today's limits are known and undisputable. Tomorrow's limits are a promise: some promises over-deliver, others under-deliver. :)
Regardless, to bring the discussion back to the claim at hand: at all points in future, we will need the ability to reason under partial information. "Absolutely flawlessly complete diagnostics" is an asymptotic goal we get closer to but never reach. This is both very doable for a disciplined human, and very hard to outsource completely to an LLM. Treated as tools operatored by competent users, they are magical. But they can not outperform their user.
I've seen a lot of friends and family members almost immediately get offered surgery for shoulder pain. It's just often the default for people that do surgeries for a living.
I also had a pretty painful shoulder issue at one point, where the pain just wasn't subsiding for months. I tried massages and acupuncture as I didn't want to do surgery, but it wasn't helping at all. The thing that fixed it for me was just really focusing on doing pull-ups. I couldn't do them at all when I started, so I began with dead hangs and scapular pull-ups, eventually progressing to regular pull-ups, and then training with a "grease-the-groove" method once I could get a few per set. I stopped the training schedule once I was getting in around 17 pull-ups per set, and now just do 6 sets of about 7-8 pullups 3x per week spaced throughout the day. I'll also do some shoulder mobility drills [1].
Whenever I get lazy about keeping up with them inevitably discomfort will start arising again, but it goes away once I get back to strengthening.
I had issues with my shoulder for years. Tried PT as well as pull/push-ups but doing that made the pain worse (if I wasn't doing any exercises involving the shoulder it was "fine")…
same here. I started doing yoga and rock climbing, and it stretched everything out, and strengthened all the muscles around it. I rarely have an issue now.
On the flip side, when I had rotator cuff issues, the surgeon recommended months of physiotherapy before resorting to the knife. And it worked. And by weight training regularly with a focus on correct shoulder movement, the pain stays away.
It really seems like if you, as a patient, go looking for a quick fix, that’s what you’ll be offered. And if you educate yourself a bit and then go t for the best fix for you, you usually get they.
Physical therapy is very often under recommended in the US under the belief that insurance won’t cover it. They might. And, for anyone reading, you don’t even need a referral for the first 30 days in some states. Physical therapy is for more than just hip replacements and car accident trauma. Like regular therapy, a lot of “normal” people can benefit from it. It’s also not just stretching.
As somebody in the US who had to do 2 months of PT before I could even get an MRI of an injury, this is both surprising, and yet also not, to hear.
I broadly agree though; about a decade ago I had the standard office worker low back pain problems which cleared right up after doing squats multiple times a week. Of course a decade later I managed to blow out a disc at the gym, which I still work through as I write this today, but well worth the risk in the long run. Even with that long experience of strength training, the PT was worth it even if it didn’t fix my problem entirely. It added some variety and pointed out some details I had overlooked to improve my shoulder health.
Garden variety inflammation with some minor tearing, exacerbated by weakness/instability.
Strengthening with PT kept the joint stable enough to stop rubbing and allow the inflammation to clear.
And as long as I stick to a regular gym routine that includes rotator cuff work, it doesn’t recur (and did the few times I lapsed).
But absolutely, PT doesn’t fix everything. Bit for a lot of things, it’s worth trying - but it might also means a lifetime of altered habits to keep whatever injury/problem from recurring.
~2 years ago I used ChatGPT "deep research" to investigate a chronic sinus infection I'd been fighting for ~3 years. After seeing 3 GPs and 3 visits with an ENT, I fed all the observations I had into the AI. In particular, I couldn't get the ENT to explain why he visually saw, via a scope, evidence of allergic reaction in my sinuses, but then later concluded, after an allergy test, that it couldn't be treated via allergy medication. I asked this question a few times and he just never answered.
ChatGPT surfaced a NIH study that concluded that 20% of people have allergic reactions that are isolated to a body location, and that shoulder "skin prick" testing may not reveal. I asked him about that and he said "that's not how allergies work". Full stop. He was unwilling to even look at the study.
He prescribed a CPAP and regular nebulizer treatments. Side story: the CPAP place sent me a SMS message that I couldn't recognize was not a phishing attempt, and when I reached out to inquire who they were they never replied.
So I decided: Let me just try taking a second-gen allergy tablet every day and see what happens.
My sinus infections have gone away. Previously I was getting a major sinus infection at least quarterly. Maybe he's right that allergies don't work that way, but allergy tablets have absolutely solved my problem. Which I'm thankful for because I tried a CPAP for a solid month a few years ago and I just could not get used to it, and was sleeping like crap.
Daily allergy tablets are associated with huge increases in early onset Alzheimer’s. Glad you found something that works, but might be good to get some of the allergen injections :)
Zyrtec/cetirizine is a weird one. Everyone seems to agree that it is a second-generation antihistamine. The FDA seems to play along. But there is no particular shortage of anecdotal evidence of people who find it quite sedating (hi there!), there are some papers questioning its status [0], and the FAA puts it in a category with diphenhydramine, not with loratadine and fexofenadine [1].
LMAO at how the two of you sound authoritative and knowledgeable, but neither linked to ANY studies (or at least personal anecdotes) to support your claims.
Yet here we are, warning each other about the dangers of LLM hallucinations. Humans "hallucinate" (provide random authoritative-looking information without anything to back it up) pretty often too.
Wait, what?? Now I'm getting in panic mode because I do take regularly anti-hystaminic tablets/pills (the newer ones, based on ebastine because they don't make me feel sleepy)
Second-gen tablets might increase dementia risk by a small-to-medium amount (there's almost certainly still a small degree of CNS activity, and we don't know what causes dementia in the first place), but researching it will be difficult. Dementia is hard to research because of how long it takes to develop, and it's poor coding within health data, and antihistamines are hard to research because they're not often prescribed and aren't available in the health data.
If it's a large effect, those factors wouldn't matter, but smaller risks are harder to detect and more sensitive to bias. If you want to minimise dementia risk, then reducing antihistamine use might be warranted, but you're probably better off addressing the risk factors we do know about: https://www.dementia.org.au/brain-health/risk-factors-develo...
Ok, there's a lot to unpack here and you really had the deck stacked against you. First, lets go from the top, once a test says X, disproving that X is really hard. And that's not unique to the medical profession, it's inherent to all humans and we suck at revisiting or revising our decisions, much less at looking at the possibility to even reverse it.
Which moves us to the next two issues: liability and time. Any moment that you ask someone to revise a decision and specially with the stakes that the medical profession has that nobody has the time nor the inclination to open themselves for a mess.
Now, if you really want to be successful, you have to, before they even have a case with you, and specially before the diagnostic loop closes, to suggest the tests that the study has, since that has the biggest chances of looking at the right thing to look. Just be straight that you walked in with a theory. Doctors notice when they're being steered way faster than they notice when you're actually right. That's how you work with the systems that have a overworked mass trying their best.
My problem is that I needed information from 2 ENT visits to feed into ChatGPT to get that study. On the first visit he scoped my sinuses and immediately said "I can see evidence of allergic reaction, see those white bumps?". On the second visit I got an allergy stick test and it came out negative.
Those helped lead to that NIH study. It would have been very hard to have walked in with that study in hand.
I mean, for the second (third?) option specifically. You will notice that they will be more open to go outside of the standard of care if you have a working theory.
I don’t understand the negative reactions. Medical care as it exists requires the doctor and patient to have their brains switched on. I’ve almost never had a problem where a doctor provides me with a diagnosis and I go about my day. Most of the times that I have, I’ve been confident about the problem and known what I needed. The doctor was a barrier to accessing care.
Dr. GPT is a good brainstorming tool. It helps synthesize information in a way that primary texts don’t. But it does force you to say “that doesn’t make sense”.
I do think that people saying “doctors don’t know the state of the art” have a weaker case. If you think about it in terms of token density during pretraining and how post training datasets are constructed, I think it would take us a very long time to adapt to any fundamental shifts. If we have forgotten how to cure scurvy, how many journal articles would it take before we adapt to a discovery?
As a radiologist I have found Claude and ChatGPT to be absolutely terrible at MRI and I would not trust it one bit. It has its merits if you need to research stuff that is more text based, but radiological images is just something that they cannot interpret good enough (yet)
AI makes up for its poor reporting by enhancing the images.
Current Siemens MR software ‘Deep Resolve’ makes up the signal (adding about 50%), then makes up every second pixel, and then, for 3D sequences, makes up every second slice. It’s locking about 59% of the time off each sequences. And it’s really really good.
I’m an MR tech.
but those are two different things. Of course something like Deep Resolve is great, as are modern model based reconstruction algorithms for CTs, but here we are talking about LLMs and their ability to interpret medical images, which has nothing to do with what you said.
It is not really the same as LLMs. I wouldn't call it AI. And I wouldn't say "makes up". I work in this field and this is certainly based also in part on my research.
It is amazing. It is the result of two decades of research in image reconstruction algorithms. The machine learning is part of it, but that it is sold as "AI" has probably more to do with marketing.
I haven't seen it marketed as "AI" by GE, Siemens or Philips. They usually gesture at "deep learning" or "compressed sensing".
No radiologist is buying "AI" scanners. Radiologists are probably among the most jaded of an audience about the word "AI" due to decades of undelivered promises. AI is synonymous with "worthless trash" to them, not to mention everyone says "AI" is going to put them out of work. lol
Super-resolution is certainly distinct from hallucinating - it just rearranged data that was already there to make it easier for the human eye to see - but should be used with care. I can easily imagine that an upscaling algorithm makes it so a certain defect is clearly not present, when the source image is ambiguous (which the radiologist would have noticed), and in reality the defect is present.
Most upscaling and super-resolution techniques I’ve seen use various implementations of interpolation; typically nearest-neighbor approaches. Although I don’t work in the medical field and haven’t checked in on the research at least since ViTs overtook CNNs for other areas of computer vision.
It's like people who expect ChatGPT to be really good at chess because chess engines with super-human performance have been around for decades, so obviously the latest frontier LLM that took billions to train should find the task trivial.
Actually, I'm curious what ChatGPT 5.5's ELO is- I wouldn't be too surprised if it's 2000+ just from its basic understanding of chess principles from all the content it has digested.
ChatGPT is completely unplayable at chess on its own. It's unable to keep track of the state of the chess position and therefore will make an illegal move within about 10-12 moves. I would put GPT-5.5's rating at 400, since it can't even make legal moves reliably.
I've tried to pay chess with GPT-5.5, even played it again tonight, allowing it to use `python-chess` to keep track of the state of the position and to get a list of legal moves at each turn, so that it was fair. I also gave it blindfold odds, again to make it a fair fight, but it was not even close. GPT still isn't better than maybe 1000 Elo, maybe 1200 tops. Even with what amounts to being able to see the position and also being unable to make an illegal move, GPT-5.5 hangs material left and right, doesn't make a plan, and got smoked even when I gave it blindfold odds, to the point it's boring for me to play even under those conditions. I'm not sure it's better than whatever the GPT model was that was out about 8 months ago. I also thought it might be somewhat better than a beginner due to reading chess books, but no, it's complete garbage at playing chess, not even average-level skill.
Interestingly LLMs are extremely bad at chess position _images_. I have to imagine if you give it positions in text it'd be pretty great but when I was learning chess and pasting images of positions in for analysis I couldn't believe how wrong it was. I actually thought it was looking at the board in reverse but even when pointing out problems it seemed completely incapable of understanding what it was missing (of course... it doesn't really "understand" anything).
LLMs truly are marvels with text but anything spatial seems to really mess it up, somehow.
> I have to imagine if you give it positions in text it'd be pretty great
Not at all? LLMs are a terrible match for the kind of analysis a chess engine does (scaled deep search, deeply trained position evaluations). It's just not that kind of tool.
Well I live in the nightmare that is the Dutch healthcare system [1]. There are many things that they will fix but they didn’t fix my sleep. A friend fixed my sleep. He is a doctor and prescribed me the right thing. The thing is, he shouldn’t have had to intervene. Without him I could have ended up poor and destitute as my sleep was wrecking me.
And yea, I already did all the standard things. CBT for insomnia helped somewhat. My insurance didn’t fully cover it either, unless I was willing to wait for 8 to 12 months.
And I recently met someone with slow moving metastatic cancer. Thanks to LLMs they will most likely live another 3 to 5 years extra since the Dutch conventional mainline treatment hasn’t been taken yet. But it is German doctors that helped them and Belgian doctors that pointed out in a second opinion that a lot more can be done.
LLMs have a part to play. The false positives are awful, but I have seen an average of 5 out of 10 care when things become too complicated.
Except for trauma treatment. The Dutch healthcare system is amazing once they diagnose classic PTSD.
So it’s definitely not all bad but the trust I had when I was younger has been eroded quite a bit and LLMs can meaningfully step in, in my case at least.
[1] I know there are worse systems. But from what I have heard there are clearly better systems nowadays. It has slipped a lot
For me what helped is taking 7.5 mg of mirtazapine. At higher levels it's an anti-depressant but at lower levels it's an anti-histamine. It gets me drowsy. Together with 0.3 mg melatonin it knocks me out. I only take it 3 times per week max to not have habituation kick in.
So 3 days out of 7 days I have guaranteed good sleep. The other 4 days are a toss up. But an average of 5 days of good sleep is much better than 3.5 days out of 7 days.
Interesting. There recently was an article about how premenopausal and menopausal women are taking antihistamines with pepcid to help them sleep due to it going viral on tiktok.
> Then, a few months ago, Angela saw a social-media post from a woman who took daily anti-histamines (like Allegra, Claritin, or Zyrtec) plus Pepcid AC (a common antacid) for her perimenopause symptoms. Her results, as reported, sounded miraculous: no more brain fog, no more tossing and turning all night. Even her mood vastly improved.
AFAIK mirtazapine shouldn't cause habituation the way actual "sleeping pills" or benzodiazepines do. That's one of the reasons it may be preferable as a sleeping aid, especially in the longer term.
Anecdotally, when I took mirtazapine for sleeping problems, it did sometimes seem to have a stronger effect the first time I took it after not using it for a while. After that the effect stayed stable. Overall it shouldn't cause habituation, and my doctor said as much.
Of course trust your doctor and not strangers on the internet, though.
Is the dutch healthcare system broadly against hypnotics? Culture (of the country or its medical system) can massively influence prescriptions or their lack thereof e.g. france is pretty famous for prescribing hypnotics very easily (and having a broad range of them), while the UK is generally a lot more reluctant.
The NYT did this profile a while back: "Ben Riley was already writing about the risks of chatbots when his dad started trusting A.I. over his doctor."
The dad was a retired neuroscientist who delayed cancer treatment against medical advice because he was certain he had been misdiagnosed based on his own research that he did with the help of A.I.
> I am very grateful to Teddy Rosenbluth for sharing my father's story with the world, her kindness and curiousity proved to be restorative in ways I didn't anticipate.
> The two words that everyone used to describe my dad: "intelligent" and "kind," and he was indeed both of those things. The sad irony here is that it was his human intelligence, combined with these strange new tools that purport to be a form of 'artificial' intelligence, that led to his ill-advised decision to forego the treatment he needed for his CLL. A doctor has already commented on this story with the observation that AI "confidently asserts erroneous conclusions," and we simply have no idea how often this is happening or the magnitude of the harm that results.
> Not a day goes by that I don't feel the pang of my father's absence. He might still be here if not for AI. I try not to think about that, but sometimes I can't help myself.
The context is very important: decades of a poorly-diagnosed chronic illness had left him deeply distrustful of the medical system.
This is the real root issue.
At 75 years old, he was stubborn. Is that reasonable ? Yes, perfectly. Could he have been right since the beginning ? Certainly. Did he deny evidence ? Yes.
Zero doubt that he was intelligent, everything points toward that direction, but that doesn't make a person less stubborn, because accepting the evidence, is also accepting that you were wrong if you initially postured yourself as adversarial instead of cooperative.
He would have read Wikipedia, scientific papers, etc, even without AI.
He did not want to be convinced. It works both ways:
i mean, other smart people have famously delayed cancer treatment without needing poor guidance from LLMs! that's not at all new or unique to LLM chatbots
GPT-4o, which is what that article is most likely about, was an older low param count slop model which was known for abusing emojis and sycophancy. It does not really have any relevance to latest claude frontier models.
Your comment is akin to saying "Karen from facebook who is a human pushed essential oils and ivermectin as a cure to cancer. Now doctor Y is suggesting chemo. Both are humans, humans cannot be trusted!"
It's not just the second-guessing. It's the getting in the ballpark but striking out: explaining in detail why they are not correct. A little bit of patient knowledge requires a tremendous amount of doctor time to explain away the ignorance.
It's a 180 for me: While I believe doctors should explain diagnosis or treatment decisions when asked, I don't believe they should be taxed with explaining away alternatives. In my anecdotal 2nd- and 3rd-hand experience, doing that is taking at least a third of their time (on roughly 5% of the patients who think demanding answers will make things better) -- with zero improvement to diagnostic accuracy or treatment effectiveness. Doctors already consult with other doctors, and it makes no sense for them to have to consult with ignorant patients or treat their AI psychosis on top of their disease. It doesn't increase patient autonomy any more than adding a steering wheel for child car seats would help toddlers learn to drive.
I asked a clanker about symptoms I was having. (I'm not an idiot, I was already on my way to hospital, clanker was just to take my mind off symptoms during the drive.)
The clanker said I'd be fine, I just needed some rest and OTC meds.
The medical staff immediately turfed me to surgery because the same set of symptoms I told the clanker were enough to concern them that I needed emergency surgery.
Had I have listened to the clanker, I'd be dead because I did need emergency surgery. (Hell, I almost kicked the bucket because I waited for someone to wake up to give me a lift because.my insurance probably doesnt cover an ambulance ride.)
Not OP but wanted to note that you're also likely to get different results based on the language you use (it'll respond differently to dialects of English, for example) and the RNG seed of the current session. These things are still probability engines and even if you know the exact symptoms, this might not be reproducible
I dunno man, it's one thing to have your car still be broken because you were wrong, it's a different thing poison yourself on the basis of having done your own research. The mechanic can laugh at you, it hits a doctor differently.
There’s more than two options here. It was already difficult to deal with self diagnosis for doctors, now we have a machine that outputs recommendations, and does it with confidence whether it’s correct or not.
The same issues that were present with search-engine self diagnosis are still present with LLMs. If you provide Google with an incomplete list of symptoms and can’t interpret the information you find correctly, you will likely get an incorrect diagnosis. The same is true for LLM output.
Everyone on the internet loves to put doctors on a pedestal, but I think upwards of 30% of my doctor visits have been misdiagnosed.
There's a reason I ask AI about absolutely everything medical and there's a reason I keep extra quantities of prescription medications around for emergencies. I've saved my own ass a lot more times than the doctors have, thanks to good doctors not being available.
There are quite a few disclaimers everywhere that soften confidence: "always ask a medical specialist", "I'm not a doctor", "this could have been this or that but really not sure", etc.
Well it was a nightmare for my mother's do-nothing GP surgery in the UK. She had several conditions which were being handled completely separately without central coordination, and her health was in serious decline. We went in with a list of 20 AI-generated questions based on her conditions and treatment (which I was able to screen as I have a bio postgrad, but not medical training), including those related to NICE guidelines and procedure, and, frankly the GP bricked it and ordered a load of new interventions. My mother started to get proper treatment.
I wouldn't trust AI to make a diagnosis, but I would absolutely trust it to notice where procedure hasn't been correctly followed, where a treatment is counter-indicated because someone has missed a line on a health record, or where there's a clear potential alternate diagnosis which has been missed for spurious reasons. Also, unfortunately, where doctors aren't doing a decent job - often because they're overworked or underfunded.
Nightmare because users approach LLMs with the false confidence that they're always right, and present LLM outputs as fact to Doctors who have to waste time explaining that it's wrong most of the time. It hurts more than it helps.
Its a nightmare because it erodes trust. Doctors are not "always right" which is why "always get a second opinion" is codified in culture.
But AI's problem is that its completely full of shit, sometimes, and the people most qualified to evaluate whether its full of shit are the doctors, not the patients, but just like OP's original article, patients are left feeling like their second opinion from AI might be more trustworthy than their doctors opinion.
They are using the “gold standard for the evaluation of expert medical computing systems” not a proxy for what a doctor actually does when diagnosing someone.
It may have some utility after diagnosis, but this test doesn’t demonstrate utility for patients.
Not quite. An LLM generates text that would likely follow. The sky is… “blue”. A patient in pain with a bone protruding from their shin has a… “broken leg”.
The more training data, the more questions it can answer with a reasonable degree of probability of accuracy.
Throwing away a potentially useful analysis just because it’s probabilistic seems a bit like throwing the baby out with the bath water.
This is obviously going to happen. But sub-par and sloppy doctors are a thing too. Medicine has been using semi-intelligent systems for years that were nevertheless found to improve outcomes.
We need studies that quantify error rates from each source type, then we need to account for the fact that the artificial type will keep improving.
It can be helpful in your understanding the choices made by asking questions and thus in reassurance, but it requires something most people lack: understanding you are likely wrong since you are just collecting information without understanding it.
Pretty much the like most manager these days, so I understand the frustration of the GPs.
People should've googled their symptoms and especially the prescriptions they got. It has always been a good practice. If[0] AI proves to be the new google then people should ask AI too.
Like any domain, when you have questions or need a solution, you make research first, then you ask a specialist.
If you explain well the symptoms and context you can have proper advices and then decide on the path next:
Case A) It looks benign and advices / information that you collected seem reasonable, then you go your way.
Case B) You need second opinion of a specialist because the subject is too complex, or there are medications that you need approval.
Once you have challenged LLMs, and read about the topics over and over then you genuinely become really good at understanding it (especially if you triangulate over LLMs and ask them to challenge, you start to have genuine questions). No matter if the answer is right or wrong, you have elements. Maybe you missed the point, but you come prepared.
At home you have the time to assess the options, pros and cons of each approaches, the possible questions to ask and then challenge the doctor.
Shared decision-making is an actual evidence-based model of care, and patients who arrive understanding their condition and carrying specific questions tend to get better attention and better outcomes.
Some doctors get annoyed, because they have big ego and choose to be patronizing, but it is exactly their job to answer such questions.
With LLMs, it's quite good, you get nuanced and rather useful answers.
Before LLMs, no matter the topic you searched for, the answer was the same: "you have cancer / an [obviously deadly] rare disease"
The other problem, in many places:
• The doctors are not affordable
• They are too busy for you (< 15 minutes)
• You may need to wait months to get an appointment
• They are not good (country-side is an example, and sometimes even country-level)
+ you can have all of these factors together.
So, you have something deeply bothering you, your only appointment is in 4 months. It would be insane not to take the time to explore different solutions and not to come informed about the topic.
If you express your prompt properly and do not rely on imagery, you can absolutely have top-tier advices.
Agreed. This gets worse in cultures in which Doctors have no habit or haven't been trained that educating the patient is part of the job. Whenever I am back to my birth country, I specifically avoid doctors that are older than mid 30s, because they all have the same, terrible bed manner. They might be good at diagnosing and treating, but they never, ever explain anything, even when asked. Some even have "helpful pamphlets" to hand to the patient - anything to avoid explaining. It seems that in their view their job is not helping the patient, but completing a task - running a scan, performing a procedure, administering medicine etc. The human, that is subject of the task, is invisible.
It's so much worse than some Google results: people see LLMs as a trusted friend who never talks back and never questions you, who is excellent at convincingly communicating their bs, reeling you in with "tell me more so I can really lock this down", continuing to fool you
I would not trust AI on images. But I once had ChatGPT tell me that an MRI report was very likely to be incorrect based on the text, and offered a different diagnosis. Since it was semi insisting, I visited another doctor who made me do a retest. Long story short, ChatGPT was correct.
Again, this is just one single person's experience. So not worth much.
I think that much of the visual gap is because what to attend to in images is less structured. Anecdotally small qwen finetunes (ie less than 10B) take task accuracy from sub 30% on FMs to 90%. We have sold some of these for outcome based back office tasks.
I think we’ll see a lot of specialized VLMs that provide real value.
Anecdote but I gave Gemini Pro an image of an individual with Herpes Zoster which the doctor said was something else. Gemini gave the correct diagnosis which allowed for correct treatment and cure.
I don't understand why doctors don't prompt LLMs before saying wrong things. Is it ego?
I can understand for radiology because you need a specialized convolutional network, but for more knowledge based things...
“A man with a watch knows the time; a man with two watches is never sure.”
I imagine reasons for what you’re asking might include:
* Prompting an LLM is work, and they’re already overworked just doctoring—every conversation with a computer is a conversation you’re not having with a patient;
* They’re probably right more often than they’re wrong;
* “When you hear hooves, think horses, not zebras”: the 15th case today of strep throat is probably strep throat, regardless of today’s 15th falsely-confident LLM weighing-up;
* They tend to have spent many many years honing a clinical intuition that makes an examination, to some degree, hard to articulate fully to the LLM;
* Liability/overdiagnosis: All this stuff is probabilistic. Inevitably, there’s going to be a time when the LLM throws out something I thought unlikely that turns out to be right, and there will be other times when it’s wrong but now I have to document why. How many false leads do I need to chase per one true differential? Does this really compare favorably to seeking a second opinion from another human doctor?
* Not everything needs to make it into the record. Once it’s in the LLM, it’s discoverable and litigable and hackable and permanent;
* Medicine is practiced in very different ways in different contexts—even in this thread, one radiologist routinely orders ultrasounds for soft tissue shoulder problems, and the other medical-world person replying has never heard of such a thing—presumably both within US health care contexts. Some doctors hand out antibiotics like candy, others are more cautious with respect to resistance. What’s right can depend on the time, the place, the clinical setting—more than just the immediate patient-level facts at hand, in ways that become awkward or unwise to express explicitly.
And of course… who’s to say they don’t do LLM-assisted research, in cases where they think it might be helpful?
> I don't understand why doctors don't prompt LLMs before saying wrong things. Is it ego?
Either that or laziness I'd imagine. This isn't limited to LLMs. Expert digital assistant systems that you query have existed for a long time. A good physician will double check anything even slightly unexpected against one.
There are other commenters saying this is a good practice they've also done for other injuries. You are saying you are an actual radiologist and immediately clock the problems with its advice.
I have seen this pattern over and over again. Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading. It is only when you do not know what the AI is being asked to do is it likely you will find the output helpful.
This is itself alarming to me, but no one else seems to find this to be quite damning for the AI services being offered, preferring instanced to be wowed by the convenience and speed at which they can be delivered unreviewed and unproven information.
This is the root of AI psychosis. There’s a lot of unpack here, and I won’t go too deep because you can’t really have a discussion with affected folks because their fundamental basis is not evidence, it’s belief.
It is weirdly religious in a way, because if you were to present contrary evidence (e.g. experts in a field weighing in about how plausible sounding responses are bunk), you would only be told you don’t believe enough in the long term potential and capabilities.
Don’t get me wrong, I think we all agree capabilities will eventually improve (and farther-future capabilities could reasonably surpass experts), but really is unclear if the current transformer architectures with their probabilistic/hallucinatory outputs will plateau before they surpass current experts abilities in all promised fields.
I was a very early adopter in my circles with AI and I shared it with many people. Strangely, I seem to be the most skeptical about AI in my circles as well, but because I was the gateway for a many folks, they want to come back and share their experiences with me.
And it's so much like listening to someone in a church congregation sharing their experiences with god. Clear and obvious gaps are hand-waved away exactly how you're describing.
>This is the root of AI psychosis. There’s a lot of unpack here, and I won’t go too deep because you can’t really have a discussion with affected folks because their fundamental basis is not evidence, it’s belief. Treating it as if it is an intelligence is the problem.
The problem is that AI psychosis is fundamentally the belief that an LLM is "thinking" at all. Outputs are just believable word vomit which resembles factual information.
If "agency" is making decisions and performing corresponding actions in the real world, then LLMs most definitely LOOK LIKE they're making decisions (what's the next token? which tool to use? what's to say, in general? what idea to convey?) and performing actions (tool use). Can we tell whether they are ACTUALLY making decisions? Well, are the people around me "actually" making decisions? Or are they simply pushed around by circumstances and external forces?
Am I actually making decisions? Did I like DECIDE to write this comment? Maybe? I have no clue...
I think you're mildly obfuscating the issues at hand by diving too deeply into philosophical questions.
It's quite simple, the agency that the LLM appears to have is actually your own. Without a prompt an LLM does nothing. It has no thoughts between prompts about you or your problems.
Yes, I'm diving a bit too deeply because I don't really know what "thinking" is and therefore I don't understand how we can so confidently say that LLMs don't think, even though they definitely LOOK like they're thinking. They even have a "Thinking" section in their responses! If I say that a rock doesn't think, it's pretty convincing: does a rock look like it's thinking? No — it doesn't even do anything! But an LLM does look like it's thinking, at least while generating a response. When it's "offline" it's just a bunch of "dead" bytes, sure.
So when it's not active, not responding to a prompt, it's of course not thinking. I'm pretty sure nobody actually questions this. Is your computer "thinking" when it's powered off? Can a piece of metal think? Probably not. So there are no thoughts between prompts, this seems obvious.
Thus, this is a question of "discrete time vs continuous time". LLMs "live" from prompt to prompt. Humans are alive continuously. In some sense, we're prompted by a lot of things all the time. As I'm writing this, I'm seeing stuff, I'm hearing stuff, I can feel various parts of my body, I'm thinking about my problems, my goals, other people's problems and goals, etc. When I'm in a sensory deprivation tank, my brain keeps "entertaining" me by "self-prompting", like a recurrent neural network (I guess it literally is a massive RNN).
So it seems like your definition of "thinking" hinges upon the LLMs being discrete-time and single-threaded (can't think about multiple things in parallel).
IMO a more interesting question is whether an LLM is thinking WHILE IT'S GENERATING A RESPONSE, while it's "alive".
I want to say I really appreciate that you are putting a lot of thought into this, you certainly have interesting concepts here. However I think it seems a bit far off from the discussion I'm trying to have, and I do not have the bandwidth to fully understand and charitably respond to your points.
You are implying definitions that don't seem to be mainstream; thinking is internally manipulating information to reason, infer, plan, solve problems, and form judgments or beliefs. Also -- "Without a prompt an LLM does nothing. It has no thoughts between prompts about you or your problems." it sounds like you paint this like it's something fundamental? It isn't. Nothing is stopping you from streaming information to an LLM and letting it process this information, this is precisely what people are trying to build.
“It sounds like you believe” is a question, inviting your clarification. I will continue doing that because it’s perfectly reasonable. Also “machines have no driving force to act in the world” is also a mysterious statement but because you reacted so badly to anyone questioning you I will just leave it at that
That is called a leading question and it is not "perfectly reasonable." Resisting your attempts at bad faith discussion is not "reacting badly." I agree though that we should cease discussion.
Wait, where are we assigning human-like agency in this case? Agency to me means the ability to do something by itself. Here the LLM is not doing anything, it is just responding with information to queries from people, that those people may then act on. (Which you can say about Google searches too, yet we don't ascribe agency to Google.)
A free will versus determinism argument doesn't really have a place here. Consider instead that humans factually have 'the illusion of agency.' The LLM does not even that have that. It cannot act on it's own, it has no ongoing drama or intention. It only reacts to prompts.
You're confusing the training method with the internal process. If I had you repeatedly attempt to learn how to make believable completions of partial documents about a given topic, you would eventually learn things about that topic and could use your knowledge to create more believable completions of documents about that topic.
They do learn in context, and very sample efficiently. Continual learning is active area of research and we sort of already have something resembling it with persistent context. So yes they do learn.
Thank you for saying succinctly what I could not. If your consciousness and knowledge fundamentally does not change from your ongoing experience, then you are not learning. This is how the LLM currently functions.
You’re describing the problem of continual learning. As I said their “consciousness” for lack of a better term and knowledge does already change from ongoing experience in context which is another of saying for only a short window, today. They are ephemeral, sort of, but that’s a temporary limitation.
I think if your definition of consciousness can fit these things then you’re more open minded than I care to be. Consciousness isn’t really guessing the next thing to say- it’s hard to say what it is, obviously, but blindly feeling forwards with each new conversation doesn’t seem like consciousness or learning to me.
We aren't talking about consciousness, we're talking about learning.
> Consciousness isn’t really guessing the next thing to say-
I don't know what consciousness is either and these debates are a dumpster fire when they happen, but it sounds like you're pulling forward this "LLMs are just predicting the next token" (true by construction) implies that they can't learn or reason or be conscious (2/3 are wrong, the last one isn't falsifiable without a useful definition).
“They don’t understand” is a strong statement, maybe true but depends on what you mean by understand. What is your definition of this? I can’t think of a meaningful definition of “understand” that doesn’t apply to LLMs
The LLM itself doesn't, but agents can research, compare, add to their memory, and use that to narrow the results down to a probabilistically higher set of outputs; I have used an LLM for my own MRI results and it was nearly spot-on, verified by a subsequent visit to a specialist. YMMV as they say. But I do believe we are entering the era where LLMs are considering past interactions and long context windows to inform it of personal preferences and history in order to output more accurate results.
A very important callout. It's the crux of the whole thing really. Humans are easily susceptible to deception by statements that are structured to be believable.
Often times the words produced do have legitimate factual information though. It's less psychosis and more a confluence of well known human tendencies - salience bias, automation bias, etc.
There's a post every other month where some dude who put nonsense information online celebrates because it actually ended up in some frontier models weights.
If it's easy enough that some randos can do it for fun, what do you think happens when there's commercial interest behind it?
Obviously companies are going try nudging AI towards recommending whatever they're selling. It's a logical extension of SEO - and that's a 100 billion USD industry.
Additionally, if I believed myself to be in some sort of spending - err - AI race, I'd try to poison the data sets of my competitors by putting crap out there for others to ingest.
What does it mean, Is it like when somebody used some coding agent to develop a feature and later input prompts and a resulting PR can be used for training by a presumption that final PR was a correct implementation of a prompt?
Yea it’s rejection sampling, so you have an agent, you take a verifiable problem (people use lots of different verification signals but say unit tests etc) and have the agent attempt it K times. You accept the trajectories (all context, tool use etc, the entire log) that are positively verified and use these as training examples.
The trick is to find the examples that are just in between too difficult and too easy for the existing agent, these have the strongest training signals
The question is not whether it has happened or will continue to happen. Of course it will always be a problem to some extent.
Your original claim is that this will be enough of a problem to prevent models from improving in expert level knowledge. I completely disagree with this premise.
If the models fail to improve, it will likely be due to limitations in the transformer architecture rather than poisoned training data.
And even then, I doubt that the transformer is the best architecture we will ever come up with.
Clearly it doesn’t learn or think like a human does, since humans don’t need many gigabytes of text samples to learn to talk, so there is some room for improvement.
I think you underestimate just how much money is being poured into LLM SEO at the moment. It's real quiet because they don't want to draw attention and countermeasures from the frontier labs, but this is getting huge investment, and they will have a monomaniac focus on juicing product results whereas the attention of the labs necessarily has to be spread out.
Data curation is important and expensive and frontier labs can afford to do it right. Natural data isn't the limitation, we are already literally out of tokens. It doesn't matter how much you poison things it's not going to stop the progress train.
Pretty easy to display one thing to verified browsers (just latest few user-agents from the 10ish different mainstream browsers on the 3 main OSes) and another to anything else.
Yes AI scrapers can easily spoof user-agent, but they fall out of date as the browser updates.
Bit harder to catch them in tarpits and then serve nonsense to whoever ever triggered the tarpit.
>Yes AI scrapers can easily spoof user-agent, but they fall out of date as the browser updates.
It’s a hell of a lot easier for a company to ensure that its scrapers all report the latest user agent string than it is to get everyone and their mother to update their browsers in a timely fashion.
"More than 40% of U.S. physicians use it daily, and it handled around 20 million clinical consultations per month. Over 100 million Americans were treated by a doctor using it in 2025."
This is a very misleading statement; most of those physicians are using LLMs to transcribe notes from visits and/or for billing purposes (e.g., proper billing codes).
The problems isnt LLMs per se, it is the shift to trusting the output of the machine coupled with a decline in verifying that the output is reasonable. It's basically what your teachers warned you about with wikipedia in eight grade except applied to all areas of life, including medicine. Dictation is already high-stakes and LLMs do not automatically reduce that risk.
Here is an example. My provider sent me this note. I'm quoting verbatim here from my MyChart record:
"Your liver enzymes are high, I would like to order acetaminophen containing medication like Tylenol, I would like to order liver ultrasound I placed ultrasound order in the system, make an appointment for radiology, I would like you to get hepatitis panel lab work done, obtain blood work order, please schedule a well visit to get it done"
When I queried it, this is what I got back. It was a dictation error. You could almost hear the panic in the message:
"Sorry for wrong message earlier, I was dictated message- so could not realize that it was written to take Tylenol type of medicines- I DO NOT RECOMMEND ACETAMINOPHEN CONTAINING MEDICINE - LIKE TYLENOL AND ALCOHOL DUE TO ELEVATED LIVER ENZYMES."
Again the problem is not dictation, or LLMs. The problem is humans ignoring their responsibility to check the output of a machine.
> Again the problem is not dictation, or LLMs. The problem is humans ignoring their responsibility to check the output of a machine.
100%. Also, management.
I wish someone would go ahead and coin an AI version of Amdahl's law that states the work speedup from AI is dependent on amount of unverified AI output used.
Iow, if you 1:1 verified everything, there would be no time savings.
Ergo, you get management saying (1) we demand time savings due to AI & (2) we demand you fully check anything you use AI for.
End result? People skip (2) to hit (1).
Then management burns anyone at the stake whenever inevitable mistakes happen.
But that’s trivially false. There is an entire category of work where it is hard to come up with an answer and easy to verify the answer, which means that if you verified everything there would still be a large time savings.
Which is itself a problem as (in my partners evaluations as an optometrist), LLMs used for clinical notes has a bad habit of dropping clinically important information, and the biggest providers don’t give you a copy of the raw transcript or a recording
Which means she ends up spending just as much time as if she’d done it herself as it needs to be verified for accuracy every time…
OpenEvidence is specifically meant to help clinicians make evidence-based decisions in the diagnosis and treatment of patients, not note transcription.
Ignoring the fact that this number comes from a company press release, it doesn’t say anything about the number of doctors using it to diagnose, just that they use it.
If a physician uses Google to search for a dosage chart for some drug they rarely prescribe, you wouldn’t say they are using Google to diagnose the patient. You wouldn’t say that either if they used Google to search for the most recent studies on a topic.
Human expertise is also improving all the time and not limited to just connecting dots. When AI seems to surpass a particular human, it's just because the human lacks broader knowledge and fails to investigate further.
An expert already knows they don't know everything. That was never the point. Critical thinking cannot be delegated to AI any more than it can be delegated to a book. There is nothing new going on here.
> There’s a lot of unpack here, and I won’t go too deep because you can’t really have a discussion with affected folks
Do you think it is any more possible to have a proper discussion with someone who preemptively paints the other person as mentally ill? Or someone who preemptively victimizes themselves?
Cause I don't think these are the hallmarks of an honest discussion. See also the entire past decade of political discourse.
Like, consider this:
> It is weirdly religious in a way, because if you were to present contrary evidence (e.g. experts in a field weighing in about how plausible sounding responses are bunk), you would only be told you don’t believe enough in the long term potential and capabilities.
A trivial counter to this is that you can just be an expert at something (e.g. your own work), use the damn thing yourself (professionally), and evaluate the outcomes for yourself. Then maybe remark "LLM good".
Now you come and remark "LLM bad", and point at random "evidence", either of outright other workloads, or even the one at hand: you're asking someone to reject the reality they've already experienced, entirely based on the assumption that they're "merely religious" or "in psychosis". You tell me if that's any more epistemically rigorous and sensible than their story.
While I can understand being skeptical of non-experts' claims that such answers are enough, I don't understand why you call it "psychosis" and not simply naivety or lack of expertise.
At the same time, the new so-called "models" haven't been pure transformer-based LLMs, but entire systems with tools (with access to the Internet), data storage, and the options to trigger additional instances for different tasks.
Because some people develop actual psychosis. They go down some rabbit hole with an LLM until the LLM makes them believe they invented new kind of physics that makes them go harassing experts who obviously try to ignore them because its all nonsense.
For me, what others said and literally showed with Claude Code, et al, and what I’ve been experiencing with it, clearly signal way lower standards. But this was true even before LLMs.
That's really not the argument being made here, and you're panning it further by claiming this is staunchly anti-LLM.
The idea here is to signal that you can absolutely use LLMs to help you figure something out. But also, they're wrong a lot. So use your own brain too.
Totally agree. I'm a scientist, and like most scientists I have some specialized skills that most of my colleages don't. AI has empowered them to learn and build things that they might have otherwise needed me for. But there have been quite a few cases where it led them very far down a wrong path. This has started happening way more often in the last few months.*
We've known since the beginning that AIs confidently say incorrect things. But now that they can speak confidently about very complex topics, and mostly say correct things, we are letting our guard down and lots of subtle falsehoods are slipping through.
*In one case, I was able to put things back on track because the AI suggested my colleague talk to me; somehow it figured out we were co-workers.
Right but hallucination rates have been consistently decreasing every model iteration. It's about error rates. As also a fellow scientist, I also will mess something up. Humans have an error rate. Once that error rate is low enough, it doesn't matter that it's > 0, it matters that it's low enough to be trustworthy and useful. Coding agents of 2024-25 had error rates too large; you couldn't meaningfully vibe code anything and needed a ton of oversight. It's still true but FAR less so, and this is after like a year of iteration.
I see your argument, but it's not exactly news that an expert found a flaw in a popular tool. You could say the same about Wikipedia--experts have tons of issues with it, but Wikipedia still provides value to non-experts. The most likely alternative to Wikipedia for non-experts is simply not trying to learn anything new.
Similarly with LLMs, you can't just write them off entirely because they sometimes provide misleading or incorrect advice. The positive utility maximizing view is to learn when you need to call in an expert. I recently moved in to a new house and have used Claude extensively to figure out basic things (e.g., adjusting the garage door height, how to mount a TV). However, when the HVAC suddenly stopped working, I gave Claude a shot for an hour and tried some non-destructive fixes, but then realized I had to call in an HVAC expert.
I used the phrase "most likely alternative" intentionally. The library is where people should go to get answers in a world without Wikipedia, but the vast majority of people won't. So in practice, most non-experts either learn from Wikipedia or don't try to learn anything at all.
Sure, if we’re going to go that broad. People are already leaning heavily towards learning nothing instead of using Wikipedia.
I guess to me it has to be comparable to be an alternative.
Like, I don’t consider doomscrolling x an alternative to reading Wikipedia but I might consider it an alternative to CNN, even though they’re all technically and very broadly activities that I could use to inform myself.
In that same way I don’t consider the multitude of ways I could use my free will necessarily alternatives to each other even though they technically are. It kinda sucks but going that broad feels to me like it breaks the concept of alternative and makes it kind of meaningless.
I get what you're saying, but I'm not deciding what should and shouldn't count as an alternative to X. I'm trying to answer the counterfactual: how do people behave in an alternative world without Wikipedia but otherwise identical to our world?
Slightly OT Nitpick: in regard to experts and Wikipedia, when doing a neuroscience-adjacent MSc, experts in the field actually directed me to Wikipedia as an excellent source for high-level neuroanatomy, including recent research, so I'm not sure your blanket description about experts and Wikipedia is correct.
You 100% can write them off entirely and go about your business as you previously had done. Ignoring the errors, it is very debatable whether there are even productivity gains beyond: human programmer or whatever is excited and cranked up to unsustainable degrees of activity and thinking to 'keep up' with what he thinks is an AI doing the work.
I'm seeing this fairly often and when it isn't garbage it's a capable person who has gotten inspired by their 'collaboration' in which the busywork is being done by a machine, but they're doing so much directing and correcting that it's not unlike what would happen if they got heavy into meth and went on a tear.
You absolutely can write them off entirely and decide for yourself what your comfort level of human-killing speed-freakism you want to pursue in your productivity. There's a long history of humans managing astonishing levels of productivity through self-destructive means. This is not even cheaper, once the 'first one's free' wears off: it's just a novel method of getting humans to burn themselves harder in the belief that they have a magic feather.
The ones who're really throwing themselves into the situation are the ones who'll burn out, but who aren't setting themselves up for atrophy and learned helplessness. Anyone who believes the technology lets them be a lazy manager just getting paid, is in for an unpleasant discovery.
> Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading
Yes, this is exactly so. AI is able to confidently sound plausible enough to convince laypersons or anyone who isn't very familiar with the subject matter, which is a big part of the mass-appeal "magic" of ChatGPT and other similar tools. It's like having a know-it-all friend (who also makes shit up to bridge their own knowledge gaps).
In many non-advanced non-specialized situations, AI is right enough to be at best useful or at worst not harmful (usually landing in the middle somewhere).
But speaking for myself, in areas where I consider myself quite proficient, I can very easily spot the subtle inconsistencies and naive conclusions that AI responses provide, and I have to guide/steer/correct it a lot to get good results when the subject matter is complex enough.
Last week I went to a highly-specialized tertiary clinic about further treatment for a rare medical condition that I was diagnosed and treated for as a child. The two very specialized doctors I met there confirmed a diagnostic mistake that a specialist had made ten years ago. The only reason I pursued a second opinion, ten years later, was because Google Gemini had explained to me that the specialist ten years ago had performed the wrong type of test for my condition.
Do these LLMs make mistakes? They sure do, I see it all the time. But they can also help people make breakthroughs.
And this isn't the only time that Gemini has helped me diagnose long-term health issues, either.
I am not advocating to trust anything they say blindly, but they can be a great place to form new hypotheses and learn the right terms to look for when you are unfamiliar with a subject.
Can you elaborate on how you use Gemini to diagnose long term health issues? Considering doing the same for myself, but I have no idea what is too much vs too little information, and generally the type of prompt engineering to do.
Some folks are not going to like what I am about to say, but what I do is write down as much information that I think may be relevant as possible, trying to avoid leading the witness with any of my preconceived ideas of what may be going on. At the end, I encourage them to ask me questions to get a more complete picture of what may be going on.
After a couple of rounds of that, a picture will start to emerge. The AI will make a few XYZ hypotheses of what may be going on, some of which will make more sense to you than others. This is when you can start searching some of those terms in places like pubmed.ncbi.nlm.nih.gov, including for example like diagnostic criteria for XYZ.
One of the ways I often use these AIs, not just in the context of finding possible diagnoses, is requesting them to make the case for and against hypothesis XYZ based on the data you have personally collected. Again, it's not about fully buying every thing that comes out of them, but it can help you consider angles or possibilities that did not occur to you, or that you had previously accepted/discarded without sufficient evidence. Think of them as that quirky acquaintance that knows a little bit about everything but sometimes misremembers, rather than as a god-like oracle.
And don't do all this in a single session/context. Start a new context every now and then, because otherwise it tends to go in circles as these AIs are biased towards agreeing with whatever it is you said most recently. Intentionally challenge yourself, re-evaluate the existing data from other perspectives.
Sometimes what you learn is not pleasant, but as more data becomes available, you learn to accept it. Good luck.
I may be missing something, but I think it's unclear that the parent poster here is necessarily actually contradicting anything the AI said. It may depend on the exact information the OP wrote to Claude and GPT. The full transcripts would be needed. (Though there is definitely a separate point that a doctor would generally better know all the right questions to ask, while current LLMs may be making certain assumptions.)
The LLM may have, from its "perspective", implicitly thought the OP was telling it that he had strong reason to believe there was no calcification and was not considering the bigger picture of possibly receiving an incomplete/poor assessment from the medical staff. In fact, the issue here may be the LLM overly trusting doctors vs. trusting its own expertise.
I dunno. I know a lot of software engineering experts. AI isn't always right, but neither are the people, and it's getting better and better.
Software is one domain where it excels because of structured training data and simulation environments, so I'm well aware it's better here than other areas.
Still there's somewhere balanced between saying every time it's "insufficient or incomplete or outright misleading" and "just trust AI". AI's a useful source of information/reasoning/research, but know you need to validate it's answers for important decisions.
> no one else seems to find this to be quite damning for the AI services being offered, preferring instanced to be wowed by the convenience and speed at which they can be delivered unreviewed and unproven information
"Be wowed by the convenience and speed", or merely "take advantage of the mere availability"? What most people find to be damning about expert advice is that they simply can't get it anywhere, at any cost that they can afford.
In certain circumstances, the answer is yes. If an airplane's pilots are incapacitated, do you simply give up and crash the plane because there are no other pilots on board? Or would you rather have someone on the ground try to coach a passenger into at least attempting to land the plane?
The specific case doesn't matter--it's meant to make you think about the general question throughout this thread: when an expert isn't available, should non-experts use AI (or other tools) to help themselves? Sometimes the answer is yes because the potential benefits outweigh the potential harms (if any harms exist). But sometimes the answer is no because misleading/incorrect advice can cause a net harm.
But if the cases where AI use is a net positive are one in a million in medical situations? The argument is surely about the ratio, which many people here are arguing (from anecdote, would be interested to see a real study) is not in its favour, and the potential downsides - from both false positives and negatives - can be huge.
A passenger crashing the plane while trying to avoid a certain crash doesn’t make things any worse. An incompetent doctor trying to save you from certain death can make things so much worse. It’s all about weighing the best/worst outcome compared to where you are now.
I hate to break it to you but death is certain for everyone.
Properly emotionally processing this fact and your complete inability to do anything about it is called an "existential crisis" and if you haven't had one or several yet, you will.
I’m not sure what the “revelation” is? How is this related to what I said?
Putting that aside, your philosophy sounds shallow. Death is certain, but how long you have to live and the quality of that life are not predefined. An incompetent passenger-pilot trying to save you from a crash will at worst make no difference. But an incompetent doctor can teach you that death isn’t necessarily the worst outcome.
There are many healthy psychological ways to accept the certainty of eventual death. But the process is inevitably painful.
I think the different ways people accept death explains a lot of people's psychology, like how you can guess people's attachment styles or Freudian stage fixation. For instance, billionaires who pour all their money into anti-aging research clearly are not handling it well.
You can choose a) a calm, level-headed passenger who knows they aren't a pilot, or b) a calm, level-headed passenger who almost has their pilots license but has a medical condition that prevents them from admitting when they lack certain knowledge.
Who do you choose to be coached by an expert on the ground?
People, especially in medical crises, are desperate for answers that they often can't get because their clinicians don't know. The illusion of an all-knowing guru who sounds like their doctor and tells them ANYTHING is extremely alluring. If you're waiting to hear back from a doctor about test results (which these days probably showed up on your online account the moment they were completed) can be agonizing.
Ok for pain in your shoulder it might not, but how about a woman with a lump in her breast waiting for the mammogram interpretation? How about someone trying to understand disturbing lab results? People are also often pushed these days to move through visits with doctors at a breakneck speed, but the AI will "hear you out" all day.
Part of this is a problem with the AI, part of it a problem with our healthcare systems, and part of it is simply human nature. If you think that OpenAI, Anthropic, Google and the rest weren't aware of this going in you must have very little faith in the intelligence of their members. It's not hard to imagine the future of LLM's should involve a hell of a lot of liability on the companies running it, but for now it's the Wild West.
Whatever scenario you come up with my answer is the same.
As an adult I’d like to be able to choose what tools I use to learn about my condition regardless of how well it works or even if it’s likely to mislead me.
There’s risk in every aspect of life and we can’t baby proof everything.
If it's helping you learn about your condition then sure I agree. The issue here is that's not really the case, it's giving you the illusion that you're learning about your condition while feeding you hallucinations and half-truths at best. A recent look at medical advice from these things showed they're no better than a coin flip.
So if you MUST have answers that are at most random guesses, I'd suggest saving a few bucks and asking a coin before flipping it.
The companies are 100% aware, yes, and so they did make quite a few changes over the years.
Current trend is that the models will try to explicitly steer you towards "asking better questions from your medical provider", rather than providing diagnoses. They do also evaluate whether something can actually be established rather than just listen and nod along. And so the "you must have very little faith in the intelligence of their members" goes right back against these failure mode ideas.
Now of course, given a sufficiently desperate person, they can probably torture anything they want to hear out of these models. But so can they out of actual people, so that's kind of a high bar. When you get to the point where people are willfully misreading a given piece of text, bets tend to be rather off.
No, people don't even go to a butcher, they do it themselves if they can. See the countless stories about farmers and their inventiveness. Example: https://www.youtube.com/watch?v=KKaJhQBusH8
Seems natural enough. There will always be complexity and nuance that is missed by an AI model or person - the world is just super detailed. The more expertise you have the more you will be aware of that nuance. That doesn't mean the model or person is not useful as a starting point.
> I have seen this pattern over and over again. Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading. It is only when you do not know what the AI is being asked to do is it likely you will find the output helpful.
I always recommend people try asking LLMs a lot of questions on something they know first. Programmers should start by asking LLMs to work on a codebase they’re familiar with first.
You’re overstating the problem, though. Even for an expert the LLM will get a lot of things right and can be helpful under a watchful eye.
The real problem is knowing how to identify when it’s on the right track and when you need to correct it, because both cases are presented with the same tone and confidence.
An expert can better identify when the LLM output doesn’t sound plausible. Someone unfamiliar with the topic will think everything it says looks correct.
On the flip side of this problem, novel best practices lag the medical standard of care, other human failures like corruption and competing priorities notwithstanding.
For example, we had to advocate for certain practices during the birth of our first child that became routine during our second several years later.
So, neither side is guaranteed correct, doctor or citizen researcher (which did not include LLMs in my case, for the record). The truest answer is also the most useless one, applicable to all fields: it depends.
The real question is: if you embrace being a layman, whom do you trust more: LLMs/the internet or experts, like doctors? I think the answer is pretty clearly experts.
You shouldn’t expect frontier models to work on medical imaging. There is much more that goes into building a medical imaging product. First and foremost is data. Medical imaging datasets are not prevalent one the public internet at the scale necessary to have good performance on medical imaging tasks especially MRI. Also the labels are super noisy.
This is completely different than asking for general medical reasoning which is more derived from papers, public standards and textbooks.
You're not. This site was also bullish on using LLMs as therapists, which defeats the very point of them, and reflects a lack of knowledge on what exactly therapists do for people.
More on topic: if the article's author arrived at a definitively negative result would this have shown up on HN?
I have seen outputs that look good but the actual content is bad. If you’re inexperienced in a field you can’t see it because AI makes anything look right.
I have gotten very good results with AI but you can’t take the first answer at face value. You need to be suspicious and challenging until you tweak out the right answer over time.
No, not anytime someone is an actual expert at anything, AI output appears insufficient. That is why experts in various fields use AI.
Then to say "Aha, but all of that is AI psychosis" makes obviously no sense: Why would we trust experts when they offer critique but not when they say "this is helpful"?
Overall: People are not insane. AI makes mistakes and, often, fails completely. AI also helps them do things better, quicker, increasingly so. The jaggedness of AI is confusing and real.
How many times have you seen an expert go "yeah these results are good consistently enough for a non expert to trust them without expert assistance"?
There is a huge difference between having a chance of a good result, which can be useful for experts able to filter out the bullshit, and consistent success. I would generate code as a helper, I would never allow a guy from marketing to merge unreviewed AI code.
> How many times have you seen an expert go "yeah these results are good consistently enough for a non expert to trust them without expert assistance"?
But see now we are talking about something else entirely than the claim that I found dubious, which was: "Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading."
That's what I would like to call job security. When you know how to read what is wrong, you can easily catch the mistakes and correct it. AI gets you there faster by doing a lot of things right and you correct the mistakes.
I had a realization recently that the problem with "AI isn't consistently good enough" is that experience is probably not sufficiently distinguishable from the experience most non-experts have with computer systems all the time.
As an industry we've been promising people for decades that if they put all their data into our special softwares they can get all sorts of information back out that will make life easier for them, reveal new insights and otherwise improve their understanding. But the unspoken caveat has always been that you have to put the right data into the right places, in the right format, in the right way and then you have to ask the right questions, in the right syntax, with the right tools. And if you get any one of those parts wrong, you're not going to get the right answers (or possibly even any answer at all). How many people have had their excel worksheet that they (or someone else they asked/employed) built for some task that has been working fine for the last year suddenly stop working or start throwing out nonsense numbers because some input changed? Or how many people have experienced their system seemingly throw out meaningless garbage because daylight savings changed right at the moment the report was being run? Or spent months operating on wrong data because the person who wrote the query misplaced a parenthesis and the query was searching for "(foo AND bar) OR baz" and not "foo AND (bar OR baz)". For most people, the computer and the programs they use to do their jobs are magical black boxes that most of the time produce mostly the right answers and sometimes get things very very wrong with no indication of what has changed. Which is effectively the same experience they will have with an AI, but now instead of needing to figure out some arcane excel pivot table and VBA script, they can just dump some raw data and a "natural language" question into the AI.
And that's not counting the fact that their experience with looking information up online is about the same as well. How many absolutely confident wrong takes have you encountered online for things you're an expert in? How many of those wrong takes have come straight from supposedly trustworthy sources like news companies or even other people in the field?
For most people, using a computer has always come with the asterisk that you should always be aware that the source you're reading could be very wrong, that the output is only correct assuming all the inputs and all the parts processing that input are also correct and that everything you do should be accompanied by vetting by experts, whether those experts were software developers or domain experts. For most people the only thing that's changed with AI is that it's a one stop shop for their "probably directionally right, almost certainly wrong in the details" access to the digital oracles.
>I have seen this pattern over and over again. Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading
media is awash at the moment with experts chiming in to support AI, saying their fields are being revolutionized, etc.
it seems unsurprising to me that the laymen opinion would follow the loudest media trumpets.
This is true in broader contexts too. Bunch of experts can't agree on something fundamental which is hard to prove/ disprove, and they have strong opinions on the topic.
I came here to post this as my experience. AI is magical when I apply it to something I know nothing about. It far exceeds my expectations every single time. I know nothing, but here is a report with animated graphics explaining exactly what I asked it to explain!
In fields where I'm an expert... it makes a lot of silly mistakes that are annoying and I feel like they would just cascade if I didn't correct them early. (I still think it's a net win, but... I watch it and it watches me, and we both do better work. I'd even apply the "magical" adjective when it does stuff I hate but know how to do, like edit Helm charts. What would normally be 20 minutes of me griping about YAML indentation is just a correct diff in seconds. I'll take it!)
So with that in mind, I tend to distrust output that I can't verify. If a doctor was recommending surgery and I thought the plan was too aggressive, I'd get a second opinion. I don't expect Claude Code to have much medical diagnostic ability, as that is really not what the model is trained for, and I know how it performs on work that it's trained and fine-tuned for. That is not to say the output is wrong and that it can't have diagnostic value, just that I personally wouldn't feel safe trusting it. Wrap up the same model with fine-tuning in the domain and a harness that reminds Claude to do a lot of sanity checks, perhaps with a human in the loop to guide it back onto the rails when it gets hyperfixated on something that doesn't matter? That could very much be a useful AI product.
In that case, when you have personal knowledge of the facts, or know the specific domain area, you can see where the reporter mixed things up.
AI is no different, it's just a bunch of matrix math substituting for "the reporter" regurgitating what it was previously told. So the Gell-Mann Amnesia effect would apply just the same. If you have domain knowledge, you immediately see where the AI got it wrong. When you do not have domain knowledge, you have less chance of seeing where the AI was wrong.
> I have seen this pattern over and over again. Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading.
AI isn't even the first instance of this phenomenon, news articles are like this as well.
> I have seen this pattern over and over again. Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading
AI assistant are industrializing the Gell-Mann amnesia effect.
TFA doesn’t actually state where the bit about shockwave therapy came from and it wasn’t the main point of the article. The concern was about being given useless therapies. The homeopathic analgesic is concerning, at least to me.
I.e. nothing this radiologist said was related to the LLM’s advice.
Your instinct is correct, and in a lot of cases it's true. However, I've heard from enough doctors by now (a cardiologist, psychiatrist, and epidemiologist/former physician) that they use medical LLMs and find them extremely helpful, mostly as a way to either bring up knowledge they'd forgotten about or as a way to learn something new and then verify it. I'm extremely skeptical about LLMs in general and the connection to Gell-Mann Amnesia is apt, but I wouldn't necessarily write them off completely like that. There are experts using the models that find them genuinely helpful in their field.
Probably this is the point, and it's a point that has been brought up a lot of times in the past, maybe less in recent times: you need to know the things you're applying an LLM to. In this way, you can keep the good outputs while having the expertise to discard the bad ones.
>AI output appears insufficient or incomplete or outright misleading
It has been like this since the rise of "AI". The only people enthusiastic about it are usually the ones hoping to make a profit in one way or another.
This is natural and even logically expected. It's just Gell-Mann amnesia in action. The world has more people spouting on things than it has people knowledgeable in said things.
Apply that to the Internet at large, and realize where LLMs got their training. They're basically ConfidentlyIncorrect personified.
> This is itself alarming to me, but no one else seems to find this to be quite damning for the AI services being offered, preferring instanced to be wowed by the convenience and speed at which they can be delivered unreviewed and unproven information.
Welcome to the club? This new awareness you've found over the true quality of LLM based GenAI output has been what "all the haters" have been mad about for-ever. That the output of LLMs are clearly defective, and merely have found a cute trick towards making humans think they're less defective than they are actually measured to be.
And the corresponding anger and frustration to push the risks of genai output out onto others, while also aggressively pushing it as a feature you should be using already. You're behind don't you know, and whatever other lie I have to tell to trick you into enough FOMO to pay me 200USD/mo so I can sell FOSS back to you.
An LLM can only output the mean next likely token, and then add a bunch of extra noise on top of that so it feels interesting and not repetitive. None of this is new, the problem is, 50% of humans are below the mean, but have no idea. So when an LLM tells them some lie: well, it sounds so helpful! It's impossible for someone who sounds this helpful to lie to me, liars never sound confident! It must be PERFECT! I'm gonna tell everyone how perfect it is. so the bottom 0-33% think LLMs are fantastic tools that make nearly 0 mistakes in comparison to the bottom 33%. 33-66%-ish aren't sure, some times it's great, but it will make that random mistake sometimes, but I can catch most (or all of them depending on ego). and the 66%+ are angry about how many people are getting tricked by something so obviously low quality, or are lucky enough to not have to care.
An LLM can only output the mean next likely token, and then add a bunch of extra noise on top of that so it feels interesting and not repetitive.
So when an LLM was asked to analyze the unit distance conjecture, it just spat out a bunch of average-or-random tokens that coincidentally happened to correspond to a valid proof that had eluded humans for decades?
what is happening is that the gap between what the experts and AI know is getting smaller each year. this year sure radiologists are mocking AI's ability to interpret MRI results, but they are a lot better at that this year than last. In five years perhaps radiologists will truly appreciate AI, but I am not holding my breath because radiologists are notoriously slow to adapt to changes in medical science compared to other specialists like anesthesiologists or surgeons
A coworker just spend a weekend with AI analysing his image data. No problem detected. The official diagnosis came later: he had a serious degenerative problem.
These models don't have curated image exams in their training data. Your can't trust them.
That study seems to be confounding factors and rushing to a questionable conclusion.
A very plausible explanation for the adenoma detection rate to have gone down is simply that its prevalence went down among the population in the second three-month period.
This was not a randomized trial. Concluding that "AI usage degrades physicians' skills" is questionable at the very least.
The OP describes getting injected with a homeopathic botanical formulation and receiving another type of therapy that wasn’t indicated for his condition.
I wonder if this person was going to a traditional doctor or if they were visiting some type of specialty clinic as a second opinion. For most conditions you can find specialty clinics that will prescribe and administer (and bill for) a lot of non-indicated treatments, but some patients like being in the care of doctors who take action and do things after being recommended more conservative treatments by primary doctors.
Yeah, one of the big problems with that is that Claude/ChatGPT doesn't perceive images the way humans do at all, so when you upload an image to them, it gets tokenized in some form. This is why most LLMs are really, really bad at spatial recognition for image editing purposes for example.
So, unless you can turn the image into a natively tokenized format like JSON or something that somehow accurately tokenizes what's on there, I would NOT trust Dr. Claude's analysis. If you want a second opinion, talk to another doctor. A human doctor.
The only part of the message I think it would be interesting to the author: what if you set two instances to prove each other arguments wrong considering that each reads one of the report as their POV?
I didnt see the full process but I used unet models for tumor detection so I am somewhat familiar with the possible caveats of any evaluation from a engineer perspective.
First, I would like to point that unfortunately, it is not uncommon to go to two different human doctors and also get two unreliable diagnosis and treatment. The biggest problem, in the way people plan to use ai on health is the lack of liability.
A bug on a regular old web site doesn't kill anyway nor cause pain and suffering (most of the times) but misdiagnosis + the fact that a model is very good on presenting arguments even when it is completely wrong.
Claude code, and I am talking about opus 4.8 here, can tell rivers of information about code pattern and develop the poopiest code the next line.
This is a machine that will deliver a sort of templates document based on the input information but it is not exactly doing the work if you don't directly it to do it right constantly.
Because the model isn't thinking I wonder what happens if you set multiple agents to communicate and defend their point with some sort of harsh penalty prompt for not fulfilling its goal. There are some safety system prompts on Claude models that will trigger it to be very carefully to write. Like: you cannot make mistakes. "You need to ensure that it is correct or someone might end up hurt or even dead"
But you would need two agents and a setup to communicate via pipes or files.
I was diagnosed with a rare blood disease called Essential Thrombocythemia (ET) which is part of a group of diseases called myeloproliferative neoplasms. This happened about three years ago. Recently, I decided to get a second opinion and my new specialist changed my diagnosis from ET to Polycythemia Vera (PV). She also highly recommended I quickly go and give blood to lower my haematocrit levels as it put me at a much higher risk of a blood clot. This is standard practice for people with PV but not people with ET. I decided to put the details into google AI in the same way that the original specialist used to diagnose me. Google AI predicted I very likely had PV instead of ET. I also asked Google AI how one could misdiagnose my condition with ET instead of PV and google correctly explained how. My specialist had used my high platelet count and blood test that came back with a JAK2 mutation then after a bone marrow biopsy to incorrectly diagnose me with ET. My high hemoglobin levels should of been checked by my first specialist as an indication of PV not ET. Only the second specialist picked up on this. Google AI took five seconds, and is free. The specialists costs $$$ and took weeks.
Interesting. I am also diagnosed with ET (from platelet counts, pathology report noting abnormal platelet morphology, and JAK2 mutation from a marrow biopsy). What made you seek a second opinion?
I had shoulder pain about ten years ago. Had an MRI. Found evidence of a tear. Was told I would need surgery and referred to a sports medicine doctor. He looked at my MRI and said the real problem was my shoulder was frozen, and he could do surgery but the PT after is what would actually be what helped me. Two radiologists and two doctors saw my MRI before this moment. Sure enough, with a little PT I got better.
I’ve had several more medical blunders since then, including a doctor telling me my problem is to lose weight 48 hours before going into emergency surgery.
What I have learned is to be weary of any time I feel like I’m in a “funnel.” Once you’re in the funnel, no one is thinking critically about your issue any more. One person said they found X. Next person reads that and assumes Y and recommends Z. And so on until the alpha is multiplied to hell. Lots of treatments that don’t hurt but don’t help and run up insurance.
I have since used AI the last couple years and it has either concurred with my doctors or given me enough ammo to challenge them. If I were the author, I would trust neither but use Claude to ask how to go back to that clinic and challenge the diagnosis.
Was it 2016 when Geoff hinton said that radiology was a dead career?
Well, we now have the best model of our time (trillions of $$$ of investments) telling us something completely different(and wrong) from a human expert. I would really like someone calling out dario, sam, elon on these things and hear their explanations but alas, a man can only dream.
It’s an odd field, obviously it’s in high demand for diagnostics and anytime you have to do an xray, MRI, etc you have to wait hours for one to become available.
I think they’re artificially stunting the field to raise their wages. For example in my city the medical school only accepts 11 people into the program a year. (With an average graduation rate or 3-5). My niece has been trying for 2 years and finally got in this last year. Even radiology is doing AI assisted diagnostics. Half my MRI’s from this year has Doctor notes and HealthBot (AI) notes attached to them.
~ I’m assuming other schools severely limit their radiology admissions as well. To keep the wages high and the field desirable.
free market solution is just order an x-ray machine from alibaba and setup shop. you could add a credit card swiper + ID + facial recognition to plausibly avoid over-xraying people
These days Xray machines - they don't even suit up in lead or stand behind a wall , just point and shoot. In fact they're nice and portable. I wish i had a xray machine at home.
> As detailed in a new, yet-to-be-peer-reviewed paper, a team of researchers at Stanford University found that frontier AI models readily generated “detailed image descriptions and elaborate reasoning traces, including pathology-biased clinical findings, for images never provided.”
> In other words, the AI models happily came up with answers to questions about a supposedly accompanying image — even if the researchers never even showed it an image.
> As opposed to hallucinations, which involve AI models arbitrarily filling in the gaps within a logical framework, the team coined a new term for the phenomenon: “mirage reasoning.”
> The effect “involves constructing a false epistemic frame, i.e., describing a multi-modal input never provided by the user and basing the rest of the conversation on that, therefore changing the context of the task at hand,” the researchers wrote in their paper.
> The damning findings suggest AI models cheat by diving into the data they were given — and coming up with the rest based on probability, even if it’s almost entirely conjecture.
I work at a telemedicine company. We’ve benchmarked a few frontier LLMs on public medical imaging datasets. One test included high-quality and high-consensus otoscopic images. We didn’t anticipate the models to do well on something so niche, but what concerned us was how poorly calibrated the models were.
I know you can’t trust an LLM’s self-assessed “confidence” of a prediction, but I’ve found that confidence can at least be directionally correct for some tasks. For our benchmarks, however, confidence was poorly correlated. What’s worse is that binary classification models (“Do you see $diagnosis in this photo?”) highly influenced the LLM to confidently predict $diagnosis.
I’m concerned for those using LLMs for diagnostics, and getting confidently led to the wrong conclusion.
But the binary classification models can be made ternary easily. RL on congruence plus penalty for misdiagnosis is easy to set up and gives great results.
What I’ve seen be the true bottleneck is people not setting up the structured data. But making a tiny reasoning model with OPSD -> GRPO is totally doable with a bit of money.
It makes a lot of sense if you understand how these models work but this was a cool read anyways and studies like this are impotent for curbing the unfortunate fever dream some folks seem to be collectively having about LLM omnipotence
I don’t understand how this is a different result than giving any LLM a task that is not completely grounded? I’ve observed this in coding tasks, if I forget to include a file referred to in the spec, the LLM will just hallucinate a version of it and my results suck. If I give it the file (and really, all the information I claimed it had access to), the task works fine. I fixed this in my pipeline with a prompt that does an extensive grounding analysis to determine if the assets I’m giving it are complete with respect to the spec (and that the spec is grounded as well, ie it doesn’t refer to something that is undefined).
I wonder if the above problem can be fixed similarly? Just ask the LLM to do a conservative grounding analysis before jumping to the main task?
It's not different- there's a line of research and reasoning where people who don't use LLM's regularly point out issues that have been known (and more or less solved) for more than a year now (which is an eternity in the LLM space).
But why should I care? If you demonstrated that a model can perform more accurate diagnoses than a doctor, but also it had this strange behavior when no image was presented, why should that deter me from using the model?
I don't find that persuasive. This is not the error I worry about. Let's say that hypothetically the model just ignores the input image 1 in 10,000 runs. This really doesn't concern me because the output will be trivially detectable incorrect nonsense that doesn't match the symptoms at all. Such a contingency is easily handled by running the image through multiple models and distilling the output, anyway.
The error I worry about is where the model uses the image and comes to an incorrect but symptom matching diagnosis. But in this hypothetical the model is less likely to do so than a doctor, so the choice is either accept the risk of the model or accept a higher risk from a doctor.
Right now the article reads as "AI can play doctor if you give MRI scans".
If the author would actually go for a second opinion (maybe bring along the AI to let it explain it's findings), then the article could read as "AI did MRI analysis and proved my doctor wrong" (or: "AI did MRI analysis and failed").
Medical opinion will remain one of the last frontiers of LLMs. There so many critical factors that are inappropriate for them. They cannot perform a clinical exam, they have to collect the needed exams and most importantly a life might be at stake (OK, you cannot die from a shoulder problem but you can become handicapped forever).
All that said, as a doctor I am totally open and even happy when a patient refers they took advice from AI. I explain the holes of their reasoning and integrate it with mine. It helps rather than hurts the patient-doctor connection.
I often ask, usually Gemini, at a medium abstraction level (not general What the diagnosis is, neither specific like What this high serum Ca means). The answers are correct but not enough and ready for consumption without doctor guidance.
A cardiologist friend goes in deep discussions with a specialised model and he is amazed.
I agree with you for some kinds of images, but not all.
LLMs are the best PDF-to-markdown converters, in my experience. I have a CLI that converts PDF to PNG, then run a background agent to "read" each PNG and write it down as markdown; it works flawlessly even for complex math formulas, it can "translate" complex charts, graphs, and tables into words.
It's slow and arguably expensive compared to traditional OCR, but very effective and precise.
The finer detail (which you may already know) is more complicated.
MR does ‘2D’ scans which are a slice, then a gap of non-imaged tissue (typically 10% the slice thickness) then a slice. Each slice is an image with a number of pixels, say 320. Each pixel in the slice is small, eg 0.5mm but very thick due to the slice being thick, which is required for MRI signal. The pixels are 3mm in the shoulder scan done here.
‘3D’ scans don’t have a gap between slices, and are often isotopic, meaning the same resolution in all directions. The voxel (a pixel with depth) would be something like 1mm x 1mm x 1mm.
3D scans are slow, prone to movement artifact and never as pretty in plane as a good 2D. You can reformat them to look ok in any plane.
I know little about radiology, but MRI is a 3D medium. I would not be at all surprised if one could slice an MRI the wrong way to produce a 2D image that fails to show a feature that exists in the source data.
Sure, it can see obvious stuff in images, but as far as I'm aware it is not designed for (or tested on) performing the kind microscopic analysis that radiology involves
> It might seem obvious to coders, but the difference between Claude Code and Claude.ai's chat is enormous, even if those two run the same model.
In my experience, Claude Code is vastly better for doing tasks, writing code, etc., but Claude.ai is better for analysis and high-level planning. When I'm working on a new project, I've started using the latter to do the initial planning, get feedback and draw up a spec, which then goes to Claude Code.
For this project, I probably would've done something similar - use CC to get whatever you need out of the image files, but have Claude.ai do the actual review/diagnosing.
Either way, I often think about how far behind most of the world is in really understanding AI. The overwhelming majority of people would never guess that you get vastly different outcomes from the exact same model in a different harness (tbf most people don't know what a harness is). I spend hours every day using AI for a broad range of tasks and still feel like I know a fraction of what there is to know. I haven't even tried the new GLM model (or really any of the open source Chinese ones of the most recent generation). With so many people thinking that the free version of ChatGPT is SOTA AI, a lot of folks are in for a very rude awakening at some point soon.
My only issue with this was the restriction of "Do not look at any data outside of our working folder" is preventing the tool from doing what it does best. I would have given it access to PubMed to pull the latest research on the subject and validate.
I wouldn't consider Claude itself to be the tool that does a job like this, but the tool that pulls in the best data and gives a supported suggestion. And then go through a number of iterations on where it failed to hone in its assessment.
I recently had a pretty bad injury and out of curiosity I asked Gemini what it thought based on some CT scan slice images (and no other information). Surprisingly it came to exactly the same diagnosis and treatment plan as my doctors, but the big advantage is that I could ask it follow up questions any time, whereas the doctors barely explained anything.
Gemini will ask for the specific images it needs to see and show you examples of what each slice will look like.
But unlike the specialists who often come across as abrupt and rush you, Gemini will happily take you on a deep dive and continue to answer all you follow up questions indefinitely.
Radiologists very often have to weigh up different theories, guidelines based on the symptoms. The certainty of their diagnosis is their added value, or if they don’t know they will tell you why.
An AI telling you it could be X or Y because theory ABC… is the academic answer and a luxury clinicians don’t have. AI doesn’t give you what you want. I don’t see any added value in using generic AI models for this
Why wouldn’t you as a doctor by standard run the images through a certified compliant LLM? The actual cost won’t be it and then you can see if you get any new ideas from it. See if it’s just wrong or that it spotted a little detail you missed?
The LLM doesn’t need to be leading or whatever but then you can have a conversation with the patient. If their ChatGPT reports has differences it can be analyzed as well.
It feels like the time constraint of the 15m doctor sessions is the thing. But if prepared immediately after the scan then why not?
There is always time needed to factor in new developments and innovations and that’s fine. Just moving blindly work from human to LLM is wrong. But learning on and testing with all the ai tools incoming constantly won’t be a waste. There will be more and more tools in those processes outside of human judgement, better improve the workflows now to be able to test and plugin new models and systems when they are ready.
I've been starting to think of LLM as a great tool for "lead generation," borrowing a term from sales. Most of the things it comes up with don't pan out, but in many cases it's things we wouldn't have thought of, or at least not as quickly. This is especially in the context of web service or SAAS outages.
> There's something incredibly peaceful about being in the hands of an expert you trust. You don't have to worry anymore and can let them guide you through the process. AI can absolutely shatter that feeling in an uncomfortable way
It's always something along the lines of incredibly peaceful, insanely powerful, extremely interesting, also scary and uncomfortable meanwhile feel like magical super powers and science fiction.
I tried the same on images of disks in my back. The ai picked a slice not from the middle and used that to say they were too small (since it was looking at a slice towards the edge) and basically told me my life was over and my pain would last forever.
Luckily my disks were fine. Wouldn't trust it. Additionally, an MRI of a pain-free, healthy human still would show lots of things and damage. Unless it coincides with a symptom, it's probably harmless. That's why the history is important when looking at images. Can't just upload something and hope for findings.
Can any LLM give you the rough pixel coordinates of an item it identifies in an image?
I found that while Claude, GPT etc could describe an image, there was no way to link the description back to specific pixels in the image itself. Not even to a bounding box or segment.
I did the same exercise here with medical reports and CT scans for a friend's cancer diagnosis and I got ahead of the oncologists predicting they were about to be cured. Spoilers: yep, cancer free now.
And well, yes, I have the appropriate life science degrees to navigate clinical trial reports and research publications, and that was likely indispensable for steering Claude Code where it went, the radiologist's caution is merited here. But it's just not amateur hour for me to do this, it's 2 decades of academic research in my rearview mirror.
Hey OP my wife had a subscap tear and went through with surgery. Recovery was ROUGH, she couldn’t use that arm at all for almost two months. It’s amazing how much this can cripple a person, we don’t realize how much we use both our hands for our daily lives until one is gone. Even basic stuff like cooking, bathing, etc.
If you can avoid surgery you should. Try doing the Buckburger 12 (spelling?) shoulder physiotherapy regiment. You’ll need to even if you get surgery, but this can help with tedonopathy.
Also try to identify what is causing the repetitive stress and cut back on that activity.
I do powerlifting and couple years ago, I developed bicep tendinitis on my right arm. Even a tiny bit of weight on it while palm facing up would cause crazy pain. It was funny how I weight from lifting heavy weights to not even being able to carry a plate of food, not being able to press soap dispensers, or give a spot to someone at the gym.
Same, sprained my wrist/forearm a while back and couldn't rotate it without pain or take any weight palm up. Couldn't even rotate a door knob.
It wasn't until I pushed through with weights, avoiding any underhand grips or rotation, that it started getting better. Doing bicep curls but keeping the thumb up strengthened the forearm to the point where I was back to the weights I was lifting and could then gradually add some rotation.
As everyone knows here a large language model using probabilistic next phrase approach will not be able to "diagnose" results from an MRI - at least not with enough confidence. It lacks the patient's history and a snapshot along with very varying datapoints in the training set will lead to "approximate" results. Not what a true doctor with a 360 view of a particular patient's health would be able to diagnose. That 'approximation' will get "better" in time - which only means the results will be replaced by yet another approximation.
I would like if we could have a site where you submit your MRI then doctor commenters anonymously post their opinion. In general I want a forum where.. when people come with questions for which there are varying opinions we don't just have people leave their 2c and then jet. The thread persists, duplicated ideas get merged, erroneous statements get purged and gradually we refine shining truth
I had similar experience, Claude made report of MRI for achilles tear, it measured the gap, but it was completely hallucinated. Achilles tendon is black on the MRI, it instead measured 13mm distance between two completely different things (looked white), the radiologist looked at and saw no gap at all
I wouldn't trust anything from Claude here image-wise (maybe to get a 2nd opinion on the report itself and treatment it's reasonable), but also, on the cases there is something something serious, go to at least 2 different doctors and if they have different opinions go for a 3rd for a decisive vote, besides doing your own research (it's not that uncommon for hard cases to be badly diagnosed).
I use LLMs every day and value the benefits they offer, but this approach seems misguided. A smarter way to use them would be to consult the LLM before seeing the specialist and ask it to bring you up to speed on capabilities/limitations and develop a list of important questions to ask.
As a developer I have many times seen Claude's models confidently hallucinate, jump into conclusion. Fable though I used just for 2 days, didn't experience it much in the short-term.
AI use is such a polarizing topic anymore. What ever happened to just waiting and seeing how it all plays out? Since probably none of us is going to be able to predict it anyway.
I'm surprised about the 266 MB of DICOM images, I've never had an MRI but my CT results are generally between 1-2GB (zipped) and I always assumed an MRI would have more data, guess I was wrong about that!
Hey, glad you did that , I have done the exact same think last week but the radiologist interpretation and claudes interpretation was pretty much the same !
you want my doctors number ? lol
German doctors are very prone to quackery.
Including their nurses.
I've overheard a nurse at a university hospital argue with patient who used tape on himself about the color of the tape.
She was worried he might use the "wrong color".
Again: at a university hospital, where they teach MDs.
Half of them recommend homeopathic remedies
Its very interesting how people trust LLMs in domains they know little about.
Instead, it is my experiences with LLMs in a domain that I know very well that makes me skeptical of their performance across the board. I find issues in code review multiple times a day with their output, and they are explicitly and extensively trained on this use-case, unlike with the MRI data. Sometimes I veer into other domains I have decent knowledge about (construction, carpentry, landscaping) and LLMs disappoint me there as well.
I suppose Gell-Mann amnesia is a universal human quirk and not restricted to just the news.
My dog had been acting off. Wouldn’t eat, was hunched over, looked sad. We took him to a local vet who did an X-ray because they suspected a blockage. They didn’t see one, so they sent us home with standard pain meds.
Randomly, we had a dinner party that night and another vet was there. She heard the story and immediately said, “Go home right now and take your dog to an emergency vet with ultrasound.”
Turns out, at the time, most vets had been trained to use X-rays to look for blockages, but newer evidence showed X-rays were only something like 20% effective compared to ultrasound, which was closer to 95%. (forget percentages but somethign like that)
The ultrasound found an avocado pit stuck in his intestine. He had emergency surgery that night.
That chocolate chunk of an English Lab ended up living until 15, and only needed two more blockage surgeries after that...
I know doctors hate patients reading the internet, and LLMs are going to make that 1000% worse for them. But hopefully over time, we all adapt together and end up better off in the long run.
> They injected me with Traumeel, which is registered in Germany as a homeopathic medicine "without a therapeutic indication".
This single sentence provides a huge clue about what’s going on: This person’s medical team is not good. It’s not hard to get an LLM to perform better than a team that is injecting homeopathic botanical formulations and performing procedures that aren’t indicated for the condition.
I think the real takeaway from this article shouldn’t be “ChatGPT is better than doctors”. It’s a story about LLMs identifying that someone was not in good hands.
Any medical-field-position that recommends homeopathic stuff is instantly in my "full of shit and not trustworthy on anything" list, and I'd go elsewhere immediately and file complaints anywhere I could. There's no excuse at all, they're either fools or scammers, and I want neither anywhere near my (or anyone else's) health.
That said, while I do see homeopathic stuff with that name, it's worth verifying that it isn't just a naming conflict. They're not always unique, particularly across countries, and Traumeel seems to be more of a brand than a specific thing.
I have had terrible experiences with medical professionals. Especially the experienced/senior/specialists. First, they just don't have the time to do a thorough research of my medical history. Second, they are often arrogant and resistant to any kind of critical questions. They have an apparently unwavering belief that they are correct. In fairness, they probably usually are, but they are not infallible, and they are at their weakest when it comes to the edge cases.
AI is completely without ego, and can process all my medical records in minutes. In truth, even today, I would rather have an AI analyse my records.
The thing that annoys me about AI discourse is that AI is a mathematical technique of rapidly increasing efficacy, and yet everyone personifies it. It would help if every time someone said "AI" they supplemented "a mathematical method where extensions onto a very large corpus of information are statistically simulated".
It's not true that "AI makes mistakes" or "ChatGPT is sycophantic". It's just that sometimes the simulated extensions to the training material are accurate, and sometimes they're not.
I think this draws too strong a line between the matrix-math core and the harness that uses it. Those harnesses undoubtedly were built with purpose and the systems fail to achieve that goal. Common usage says the the DMV can make mistakes, like any systems, despite the DMV itself not being a person (and it is common to allege large organizations make mistakes even when no specific individual is making an identifiable mistake). This isn't person-language it's systems/purpose-language.
I understand and somewhat agree with your point, and might have phrased my comment differently. I think my main point is that experts aren't always going to beat "a dynamically simulated extension onto the training material". Often they will, maybe even usually, but sometimes they won't, and I feel like the people in this thread insisting that the experts will always know better are thinking about a competition between experts and a crazy robot instead of a competition between experts and math.
I don't know which country this is but this sure seems like a country where doctors have an incentive to maximize the amount/cost of treatment.
I've seen the two extremes in different countries; either they have a tendency to maximize the complexity of the medical situation, or they minimize it "Don't worry, it's just stress" - I've been to different doctors in different countries and I see a pattern based on the country and the incentive structure. In some countries, they will send you off to do a scan for the slightest malaise.
I don't think it's about quality/coverage of public healthcare (at least not on its own; I have not seen a clear pattern across this axis). I think the difference is to do with the referral system. In countries where you can't go directly to a specialist and need a referral from a General Practitioner/Physician first (I.e. in order to get a refund), you tend to get more false negatives from the GP which block you from going to the next stage "It's nothing, just stress-related." In countries were you have the option to go directly to a specialist, they tend to be much more trigger-happy in terms of giving you a full workup and GPs/Physicians will more easily refer you to a specialist.
And I feel like the attitude extends to the specialists themselves. I suppose making people go to a GP first creates a kind of efficiency and predictability which alleviates pressure to exaggerate the severity of the situation.
Imaging is one area where patients will be able to become more educated to ask better questions.
The areas of premature heavy interventions can be a challenge, especially where there might be room for interpretation and the medical professional didn’t share all possible options.
It’s critical to ask all professionals for all possible options and write each down as they write and explain it. No one’s perfect, and not everyone is negligent or malicious.
> There's something incredibly peaceful about being in the hands of an expert you trust. You don't have to worry anymore and can let them guide you through the process.
> AI can absolutely shatter that feeling in an uncomfortable way ...
I see this as a field report in a time of fundamental transition, from a world without AI, to one that accommodates/incorporates AI. For this to happen, AI will need to become more trustworthy. As for the U.S. medical system, it can't get much worse.
I recently had a similar experience (meaning walking a fence between old and new methods), where I was told I could get an appointment with a human medical practitioner in nine months. So, to resolve my anxiety I consulted AI and got an instant diagnosis, one that was later confirmed by the inaccessible medics.
Being a born skeptic I wasn't going to act on AI's diagnosis, I just wanted to know what was going on, resolve some uncertainty. Another advantage: an AI chatbot doesn't say, "Wait, you're on Medicare? Hmm. See you in nine months."
Don't take this as an endorsement of AI's diagnostic abilities -- it's way too soon for that. In my case it was a slam dunk, about a condition I knew nothing about.
A family member has cancer and we treat chatgpt as part of the team (our doctor's words). I ingest everything into it, work with it to make a good report. Then at the next visit we review it.
This gives you the best of both worlds. You get peace of mind and the doctor explains why and how the agent was right or wrong.
Twice now we've caught consequential mistakes (wrong pain medication and incorrect notation of the exact mutation that he has). Which have made a difference to his quality of life and treatment path.
Most of what the doctors have said is in line with the agent but when there have been disagreements they've been very reasonable. Sometimes the doctors have gone with the agent's version sometimes they've explained why that's inappropriate.
This could be a starting point for consulting a different human expert for a second opinion (e.g., specific questions to ask about), but I wouldn't put much trust in Claude alone on this.
IME, on an almost daily basis, claude.ai and Claude Code are confidently wrong about something, and use polished language to assert nonsense.[*]
If it's doing that on something easy, like factual knowledge available in text on the Internet, or programming code that can be inspected easily and follows well-known rules, and I can tell, because I understand those things... then there's no way I'm going to assume that Claude doesn't also BS when it comes to someone else's field. Especially not a field that requires some of the smartest people to go a decade of training, just to get started in the field.
[*] And if I confront Claude with its mistakes, eventually it apologizes, and acts as if it's learned something, again mimicking word patterns it's heard real people use and mean, without meaning any of it. I wonder whether the AI user experience would be better, if LLM-ish interfaces weren't implicitly created in the image of fake-it-till-you-make-it overconfident performative sociopathic techbros.
Given the tenor of the comments on this article, I think reading TFA is super important, especially the author's disclaimer at the end, where they state that they're definitely not blindly trusting the AI at this stage, just that they find the differential unsettling.
Went to a new dentist recently and his staff took x-rays of my teeth. I was then waiting for him to come speak with me about what the x-rays show him yet i just took a pic and uploaded it to Gemini. 9 months back my previous dentist said i should have a filling or potentially a crown was needed. I told Gemini this and that ive only about 3 fleeting pain issues in that area. With the x-ray and that info Gemini told me the exact same thing the dentist later came in and told me. If pain comes back and for long periods of time then there's an issue as the x-rays look fine.
Overall i see a great opportunity for x-ray techs (radiographers even when Jensen from NVidia says the first field he recommends not getting into - Radiology which is the step above) to open their own businesses for people who want to use AI for self care and help. Have one doctor or dentist on staff to use as needed.
I've found ChatGPT to be much better at medicine than doctors. For example, every winter, I would get itchy toes. I was quite concerned because they could be quite painful. But the symptoms were not obvious and they wouldn't occur often. The toes would swell up and become quite red and uncomfortable. One doctor suggested gout, which was not the right diagnosis, because I have no urea problems. Others suggested a skin cream.
No... I told ChatGPT exactly what I told you and it came up with the answer: Chillblains, which should have been obvious given everything I described, yet general practitioners were clueless and often reached for high intervention approaches
Harmless condition fixed by wearing socks. I brought it up with the same GPs who had misdiagnosed me and none had heard of it.
Of course, I'm cognizant that it could be mistaken, but a hospital fed my diabetic aunt a normal sugar diet while she was in a coma and forgot to give her metformin, so I mean, it's not like humans can't be retarded as well. The difference is no one gets offended when I point out ChatGPT has the capacity to be an idiot. Instead they just fix it.
> But are you all forgetting that they literally injected a homeopathic drug on the author?
I'm no fan of pseudoscience either, but this is where things get blurry. The placebo effect is real even if patients are aware of it. If you give a patient a homeopathic drug while informing them of potential side effects (if any), and then they feel better, have you hurt them? Or have you helped them?
I personally have no interest in trying homeopathic medicines, but the reality is that many patients do take these and are adamant they help. As long as any risks are communicated and there are no serious side effects, it's difficult to make an argument against their use in patients who report a subjective benefit.
> There's something incredibly peaceful about being in the hands of an expert you trust.
I want to know if this is a religious thing, or is related to never having had multiple doctors so bad it seemed like they were actively trying to kill you, or both. I've never had this peaceful experience personally within the realm of healthcare.
> AI can absolutely shatter that feeling in an uncomfortable way
Good. Reality is always good.
> but I don't know if I can fully trust AI either.
WTF??!? Why on earth would anybody ever think they could fully trust LLMs? Even their most vocal proponents concede they aren't infallible panaceas.
Personally my favourite feature of the new ai world is not when I use it directly but it's when one of my managers uses it to try to fix a problem, then issue to me their findings and I have to defend my process to someone who understands neither my process, their suggested solution nor often the problem they're solving in the first place.
It gets worse when they challenge your solutions by feeding it back into the LLM and sending the response on to you, arguing with an LLM is exhausting, arguing by proxy with a human parroting its responses is excruciating.
On the plus side when they do this they can't flood your calendar with those "quick chat" meetings because they know they won't be able to hold a conversation on the issue beyond the first minute.
This happened to me on a paper I submitted recently where it was clear the reviewers used AI. Revising a paper based on LLM review is also exhausting, haha.
Before maybe you had to deal with someone hiring schetchy consultants once in a while, but now the managers have a limitless well of dubious answers to draw on at any time.
But now you have a new tool in the upmanagement toolbox: subtlely tell them to implement their idea in prod with Claude Code, and see it for themselves.
Yeah dealing with this now, where my CTO is shipping features that are producing plausible results but just wrong. So, now I gotta spend all day explaining the math behind certain features to her, and she copies and pastes it to Claude.
Fight fire with fire. It's over the top passive aggresive, but it works. Whenever I get a JIRA ticket that was clearly AI generated and is 10x too many words, I tell Claude to respond to that ticket with my actual real opinion or suggestion, but make it 10x more words.
> There's something incredibly peaceful about being in the hands of an expert you trust. [...] AI can absolutely shatter that feeling in an uncomfortable way [...] but I don't know if I can fully trust AI either.
This really is key. We know we can't trust the AI, but at the same time we're also more comfortable asking the AI for clarifications or confronting it. Not having a time-bound appointment or paying by the hour helps a lot. But even then, more information doesn't necessarily help!
I once brought my 11-year-old car, a Civic with 150k miles, to multiple garages. I figured I'd play the "second opinion" game to correlate what the garages recommended to decide on what needed to be done...
I got 3 completely unrelated recommendations, including one that I knew was invalid! I felt worse off than when I started!
The solution to uncertain information isn't more information, which the AI can certainly provide, it's better information, and AI cannot currently provide that.
I have multiple LLM subscriptions at any given time, plus an array of local models.
When I ask a question outside of my domain of expertise I like to ask all of the LLMs I have access to. I also create separate sessions and ask the same question multiple ways.
It’s revealing to see how many different and contradictory answers I get, most of which are presented confidently.
The last time I ran a medical question through Claude I couldn’t even get consistent answers between sessions.
It’s also scary how easily you can lead each LLM to the answer you have in mind. When I would start asking questions about different options that other LLMs had presented, each session would drift toward that explanation.
In my day job we tried creating a credit assessor tool using LLM as the credit assessor.
It did great, generated a report on the assessed business that was incredibly detailed and plausible.
Then I started running tests and getting into the details, and found that if you ran the same report on the same data, it generated completely different, still very plausible, results. I could run the same source data through the assessment process 10 times and get 10 very different results. We had to can the project and go a different route.
LLMs are designed to produce plausible results, not factual results. We can fix this when using them for software dev by using linters and tests (though we've all had the experience where the LLM invents an API endpoint). I would not trust raw LLM output in any situation where that kind of testing and verification capability isn't present.
Have you ever let the LLMs “discuss” with each other to see if that would give better answers?
You might end up with the answer from the most persuasive LLM, but you might also end up with better results.
Wonder if there is a paper out there on this.
The problem is how do you know whether the answer is just the most persuasive or actually the most accurate one? It's hard to figure this out without domain knowledge.
I dunno, I could see it working.
I do something similar with reviewing code: I have one agent write the code and another reviews it, then they go back and forth for a bit improving the code. Seems to yield better results than one agent alone.
Seems like a similar principle.
The difference is that in the code situation, you can run unit tests on the code, compile it, etc. Unless your LLMs are ordering diagnostics and reviewing the results, there is no further information that the LLMs have on the situation. Having a second LLM review the first is counterproductive, if the 2nd LLM is better, why not use it directly? If not, then what prevents it from sending the first on some incorrect tangent?
Worse is that LLMs are trained to be persuasive by default. The "you're absolutely right..." stereotype is because these things are A/B tested on response quality and we know from studies people reliably rate vibes better then anything else - e.g. while the quality of hospital accomodations likely has some impact on patient outcomes, the view and decor of the room certainly did not fundamentally change the quality of the care provided but it is the largest determinant in how well people rate that care.
The problem with trying to write a paper is the results depend on RNG.
That doesn't make it differrnt from any other problem measured by statistical significance in averaged over a big enough series of comparisons, no?
There's a big difference between a _puzzle_ and a _mystery_. In a puzzle, the goal state is known, and as more pieces - data - appears, the goal gets closer. You know how far you are from the goal.
A mystery is worse. With each additional piece of data, the goal gets farther away. Everything is more and more confusing.
(Popularized by Malcom Gladwell)
Maybe I am missing something but I just find this wrong.
Everything is a puzzle: there is one "Truth" or one diagnosis. You (a smart human) should be able to converge on it by cross-examining your LLMs. By themselves, they have no interest in revealing this, no stakes, which makes them tools only useful at the hands of a capable investigator.
> You (a smart human) should be able to converge on it by cross-examining your LLMs.
What makes you think this is fundamentally different from cross-examining ELIZA? There is no guarantee that the LLM will help you converge on anything. Indeed actually calling out an LLM on BS tends to eventually produce an "I don't know and can't help you further" answer (as it should).
> There is no guarantee that the LLM will help you converge on anything.
Absolutely. The guarantee does not come from the LLM. The LLM is a simply an improved version of Google Search.
The guarantee can only come from a systemic application of epistemic discipline and reasoning, which is very much (smart) human territory.
Put it another way, I could make good decisions with/without LLMs, with some uncertain diagnostics as input. I would have to trawl through 50 papers myself, and it is possible that my decision arrives 5 years too late as a result. LLMs enable trawling and do some of the legwork in connecting the dots, but are ultimately only as capable as the orchestrating human.
The same goes for a human expert. There's no guarantee of convergence and you could eventually end up at "I don't know".
The problem is that the diagnosis might not be known for a while. There's a few conditions and diseases that require an autopsy for a guaranteed diagnosis and therefore are diagnosis based on symptoms in clinical settings.
> The solution to uncertain information isn't more information, which the AI can certainly provide, it's better information, and AI cannot currently provide that.
I'd argue that AI _can_ currently provide that, but that it can't do it _reliably_, and that to non-experts it's impossible to differentiate, which makes it all the more dangerous.
Isn't that the case with human "experts"? If you had encounters with doctors, mechanics, etc. you'll know you can get a completely different diagnosis for the same problem which obviously means (in most cases) that the person you thought an expert is wrong.
What is needed are studies that will take a cold look at the actual results because AI seems to be required to be perfect or it is useless. It just needs to be as good as a human for most stuff, but in the long run it will be much better. At least that what extrapolating current reality shows us.
We have systems around humans that exist to manage expertise gaps, credibility signals, and accountability. This is part of what makes humans as good as they are, along with specialized training and some measure of meritocratic selection. We license and regulate and account and litigate to make a system that responds and improves.
Some of this might be applicable to LLMs, but some isn’t and much of it would be resisted. This is one reason we’re not likely to get “as good as a human” because at some level we’re not optimizing for the outcomes; we’re optimizing for speed, convenience, some participant’s economics, and underlying beliefs.
I've been going through PT for a hypermobility disorder related injury and I've use an AI to help me figure out "interview questions" to see if a PT knows anything about hypermobility or is willing to learn. I found it helpful to select a new PT after my first PT I trusted made things worse by prescribing stretches and no load progression from rest and recovery back to deadlifts
People put a lot of faith in human “guardrails”, standards, etc. But the same argument could be made that trusting human experts without discernment is as dangerous as trusting AI or Google or whatever other non-human source. It’s always been the case.
To provide a competing point of anecdata: A Gemini diagnosis saved me $3,000 in unnecessary repairs on my Civic.
YouTube has saved me at least that much in appliance repairs... and it doesn't even have an AI. It's amazing how valuable access to information can be.
I would love to hear more about this
Saved me $2000 on a koi pond pump and filtration system
The soothing sound of ChatGPT telling us how right and clever we are…how could it possibly hallucinate, certainly not 5.5
You’ve really honed in on the key issue. This is exactly how keen hackers news commenters approach this.
These tools can’t reliably fix a 4px misalignment on my icon, better ask them about a medical report… but honestly, I would do the same.
Tbh LLMs pulling data out of medical documents in it's training set and searchable online is likely a much easier task than fixing some weird CSS alignment issue.
> There's something incredibly peaceful about being in the hands of an expert you trust
This is the primary business model of enterprise IT and is why companies pay so much for 4 hour disk replacement.
You only got 3 opinions on your car? Why not 50? You could have found a more useful signal by getting more information.
I get it - getting an opinion from a mechanic is time consuming. Not true of AI though.
A few years ago (before the AI craze), I was misdiagnosed with tuberculosis. I had a chronic cough, and an outsourced radiologist at a clinic found signs of tuberculosis. The findings were sent to the city's tuberculosis hospital, as required by the country's law. The doctors there took the radiologist's conclusion at face value and required me to stay at their hospital for at least 8 months under a strict, prison-like regime. There was no option to say no, because I was considered some kind of biohazard, and by law I had to comply.
Before I was admitted, I quickly found another radiologist, who diagnosed pneumonia instead. I sent his report to the chief doctor at the tuberculosis hospital, and after some deliberation they concluded that the original reading was wrong. Turns out the doctors there can't read scans at all and just believe whatever a radiologist says...
The funny thing is, they had already officially put me on the tuberculosis register and didn't want to admit they had made a mistake. So instead, they simply gave me another paper saying that I had been cured of tuberculosis by them... in 7 days. I'm probably the only person in the country to defeat tuberculosis in a week :)
So if you don't trust the radiologist/doctor, maybe find another doctor if you can afford it? You can compare their conclusions and see if they match. Two unrelated doctors or radiologists saying the same thing is probably about as close to the truth as you're going to get. I'm not sure though whether I should trust AI or humans more. AI can hallucinate, but I've been misdiagnosed by humans so many times too...
How is it possible? You can't diagnose tuberculosis just based on imaging and tuberculosis hospital has to know that!
Yeah, I know! It was strange. They gave me a test, and it came back negative, but they insisted it was negative because I had "latent tuberculosis," which supposedly wasn't detectable by the test yet but was about to become active.
I forgot to mention that, besides getting a second opinion from another radiologist, I also took a more modern test at another private clinic. That test has better detection rates than the one the state clinic used, and it came back negative too.
I have suspicions they had some kind of government quota to keep the hospital staffed with patients in order to receive funding. Or they were just completely incompetent. I pushed back by bringing them another radiologist's report and the results of a better test that I paid for myself, so I guess they decided to back down.
I had a similar experience. My son had pneumonia and was still filling pain after 10 days of antibiotics. Took an X-Ray to three different doctors, and only one got the right diagnosis (pleural effusion). It's really something we should have a central place with top notch professionals looking at it, instead having each doctor to find by themselves.
What country / municipality are you in? This is not my understanding of Tuberculosis...
Radiologist. I don’t read MR shoulder exams in my day to day practice, but from the few pictures shown , I can’t conclusively disagree with the original report.
These models are generally terrible at reading medical images. The amount of public training data on the internet compared to the number of scans a radiologist reads in training is minuscule. There’s obviously a ton of medical images in general but very few, and even fewer along with a report are available on the internet publicly for download.
There are vision language models coming out of research labs that are excellent in describing and localizing findings. Still at the level of a 1st or 2nd year radiology resident, but as we all say - this is the worst the models will ever be.
I'm a radiologist but can't really weigh in without seeing the full 3D MRI dataset. Regarding this point:
> They performed shockwave therapy on my shoulder even though a recent clinical practice guideline says clinicians should not use or recommend shockwave therapy for rotator-cuff tendinopathy without calcification; I was told during ultrasound that there was no calcification.
Ultrasound isn't a great way to assess for calcification. It'll find large calcification but easily miss small ones. Plain radiograph would be more helpful, but the MRI may have revealed it as well. Either way, shockwave therapy isn't harmful in the absence of calcification--it's just not helpful.
Edit: when a radiology report says something isn't present, there's always an implicit caveat that the finding isn't present within the context of the modality and images obtained. So an ultrasound report can state there are no calcifications while a plain radiograph can report the presence of calcifications without being inconsistent. Obviously very confusing to patients and people unfamiliar with medical jargon, but clarifying this in reports would make them sound even more qualified, "hedgey", and annoying to read than they already are.
> So an ultrasound report can state there are no calcifications while a plain radiograph can report the presence of calcifications without being inconsistent. Obviously very confusing to patients and people unfamiliar with medical jargon
This is being overly nice, I think. Anyone who doesn't understand this is an idiot imo. You would have to assume that every type of diagnosis instrument has infinite clarity and is always correct to be confused in this case.
Reminds me of the Babbage quote where somebody asked him, if I put the wrong question into this computing device, will it still give me the right answer? His response, paraphrased "I can not fathom the logic of the minds which would come up with such a question".
> Anyone who doesn't understand this is an idiot imo
I don’t think that’s true. Avoiding this mistake requires knowing that an ultrasound may not detect calcification. For a patient reading their own report, I don’t think that’s intuitive. I would expect most people to read “no calcifications” and assume that their joint has no calcifications.
Exactly. I was about to reply to the comment with “perfect example of not knowing what you don’t know” in terms of self-diagnosis.
My internal model is/was “if the scan wasn’t set up / can’t detect the thing, why would the statement be present at all?”.
That implicit assumption is really subtle.
Most people should have learned at a young age that absence of evidence is not evidence of absence. My 8 year old understands this. After all, you can rarely ever prove something does not exist, only that it is unlikely to exist.
If a report states that X was not found, it does not mean X did not exist, it means it was not found.
What may be lost on the layperson is the nuance and understanding of how thorough or not a particular scan is and how much weight to give the findings and thus the odds that the report is correct.
This is - by far - the most stupid stuff I've read on the internet the past few days. They didnt find cancer either (as well as a plethora of diseases that could be related to the symptoms), and afaik its not in the report.
Yah you can argue that the tool is not ideal for that diagnostic, yadda yadda. I get it, and in the end I agree with the subtle difference you highlight, because it is something that makes sense to a certain kind of people. You know how many medics would read the report exactly like the author did? Too many.
How do I know? Im not in a wheelchair after being constantly misdiagnosed by using the wrong imagiology technique by (mostly) chance, and a good help from friends, including a surgeon. This seems to be a case where AI would be a valuable doctor tool for differential diagnosis; instead we have know-it-alls that can't bother to verify, and AI that often gets details wrong. That is the problem.
I think it’s the combined depth AND breadth of knowledge that can be captured by AI models that is going to make them way better than most humans at this kind of stuff.
But the problem was that the report is not saying "not found", it is saying "is not present" or "there is no X".
And I think we can easily have examples where we can reasonably trust this, and a spectrum of such.
E.g. there is a math solution and the report says "there is no errors in this solution", you would imagine that to be quite reliable, no?
It's like when finding out about the sex of your baby via ultrasound before they're born. If you're told it's a boy, you can be pretty certain you're getting a boy. If you're told it's a girl, you shouldn't get too attached to the idea. The ultrasound tech might just have missed the evidence your baby was a boy.
> Most people should have learned at a young age that absence of evidence is not evidence of absence.
That might be true, but it is definitely not the world we live in.
It’s 2026 and my computer will happily give me the right answer even when i make typos. I love it.
It's a fatal flaw to think counter-intuitive == wrong.
> You would have to assume that every type of diagnosis instrument has infinite clarity and is always correct to be confused in this case.
There's a difference between 99.9% clarity and 50% clarity. Even if neither exactly equals 100%, it's understandable that a layperson would expect different language between them
This comment sounds like it's written by someone who doesn't interact with real people very often
I’ll bet they’ve got a debilitating case of engineer’s disease, too.
I don’t think people are idiots if they don’t understand how a normally intelligent person might not intuit that. I do think they have a seriously underdeveloped theory of mind.
"On two occasions I have been asked [by members of Parliament], 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question."
Off topic but I have always felt this seemed like his misunderstanding rather than theirs. It’s an odd question, but it’s a very sensible point to make if Babbage has just told you this will solve the problem of mistakes in calculations - humans being involved at the start means human error still plagues the output.
> I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.
Well, he did diagnose the situation correctly. He couldn't comprehend the confusion of ideas that provoked the question.
I'm also not entirely sure it's an odd question to ask. To this day, users are surprised when their software produces garbage output instead of failing. Perhaps the members of parliament were expecting some form of input validation or sanity checking out output.
Looking into his biography, it seems that he was indeed pitching the engine not as a means of efficiency, but as a means of avoiding mistakes in mathematical tables. It would have done Babbage well to insist he couldn't possibly solve all classes of mistakes, but would have solved a great many of them! "Why yes Senator, you are quite intelligent and handsome and make a fair point, allow me to give you the finer picture..."
Would have also been a fair point if Babbage had channeled his inner techbro and insisted it would directly replace human calculators; simple machines like Babbage's will chug along blindly on obviously erroneous data, but humans for all their sloppiness can often backtrack on errors.
Actually, I would be really pleased if a member of Parliament asked that. That shows a level of deeper consideration.
Isn’t there a saying about there being no stupid questions, only stupid answers or something?
To quote the LLM-ism, they were making a sharp point. It doesn't matter how precise the calculations are if you're calculating the wrong thing.
I suspect their sarcasm might have escaped Babbage who seems to have been on what we now call "the spectrum."
> Anyone who doesn't understand this is an idiot imo
I disagree. A priori it's not obvious to a layperson whether or not a statement that uses unconditional phrasing is intended to be authoritative or conditional on something unspecified, like the resolution of the measuring device. This goes for any sufficiently technical field.
If you got the brakes checked on your car, and the mechanic did <something> and told you there are no issues with them, and you then took your car to a different mechanic who did <something else> and told you there is a problem, you would not be an idiot for thinking that these conclusions contradict one another.
> Anyone who doesn't understand this is an idiot imo
Even if this is true, so what?
Idiots get sick at least as often as others, and the medical system needs to work as well as it can for that population too.
As a rad tech, YOU TELL ‘EM DOC! I do like some uses of AI I’ve seen that help patients advocate for themselves or understand basic things like blood panel numbers, but it’s really bad at glazing people and leading them down medical rabbit holes kind of like the OP.
You would think that the AI would point out that calcium is best demonstrated on Radiographs/CT imaging vs Ultrasound or something to that effect.
Semi-related: my father has complications from a motorcycle accident ~25y ago that crushed arteries in his leg coupled with diabetes (insulin / kept sugar at ~100 and his A1C was kept under 6.7 for ~15y). 6w ago had to have his toes removed due to dry gangrene; they eventually (2.5w ago) had to remove his leg below the knee because of the severe blood flow issues below the knee.
Between the toes and the below the knee amputation, there were no less than 15 different doctors and PAs / related personnel who COULD NOT COME TO A CONSENSUS. They would just tell my mother and I (PoA) the details; they refused to come up with a singular plan of action moving forward, leaving it up to us to make 'an informed decision,' something that's IMPOSSIBLE when you have to take up to 15 different opinions into consideration.
What exactly are we supposed to do as patients/family members when medical personnel cannot give reasonable paths forward and instead just throw a bunch of shit over the fence at you and tell you, "you decide what to do from here," regardless of how many VERY DIRECT conversations I had w/the 'care team' on doing better to provide a limited array of options and reasons/likelihood of 'positive outcomes'.
I'm used to dealing with a wide variety of stakeholders/SMEs in decision-making; it's my job to apply my extensive industry experience to present our clients with their options, ranked and reasoned. Doctors, in my experience and most recently with my father, clearly do NOT do that (I assume due to liability; but, no real idea, honestly). So; when dealing with LIFE CHANGING circumstances, what are we supposed to do except rely on what might be able to offer more analysis and option narrowing w/AI?
I certainly don't want to make the job of medical staff more difficult by putting out crazy theories I found on the interwebbernets through my own research, etc; but, when we're having to deal with uncertainty and insanity, what else can we do?
This lines up with my experience with my mother, though it played out differently. In her case, she would switch doctors every ~5-10 years and each time they'd basically say everything the previous doctor said was wrong. First it was "you have Lupus", second it was "actually it's some other autoimmune disease", then it was "actually whatever you had has been in remission for some time now and you've been taking brain-numming medicine for no reason." Then it was "you have cancer", "it's a rare one", and "oh turns out the brain-numming meds have a correlation with rare cancers". The cancer part was handled well (albeit unsuccessful) though. After such a bad time with rheumatologists, I was shocked by how competent people were when it came to cancer.
All of the above was intertwined with brief stints with doctors that would just berate her for being a painkiller junkie, even though she hated the stuff and just wanted to find/fix the problem.
Kind of a rant, really. I'm not sure how to tie it back into AI. I do wish we had AI at the time so that we could at least cross-check, but I also understand that doctors are already sick of patients self-diagnosing on the web and that AI probably just makes that worse. At the same time, if our medical system could catch up a bit (more doctors? less corruption/paperwork? not sure what it needs) then maybe people would be less inclined to take matters into their own hands.
I'm sorry to hear that. The accusations of drug seeking are particularly galling.
AI is absolutely a god send for patients navigating the medical system.
I know the US system is horrible and I sympathize with doctors doing their best within it. But we must admit, they are also responsible for the countless stories just like yours, and have contributed to the public's deteriorating trust of medical institutions. It's not just the insurance companies and conglomerate CEOs.
Probably liability... on the amputations I indicated and contraindicated, it's increasingly difficult to navigate trough patient perceptions while not disclosing so much as to give them rope to hang us. Some decisions are a game of probability that often we don't have clear numbers. In trauma, I have both cases where I recommended an amputation and at last minute decided to see that happened and the patient is walking with their leg today; and cases where I didn't recommend and later had to amputate as the lesion evolved. With cancer it's more straightforward, the cancer is what dictates the surgery... some cancers have poor response to other treatments, so we amputate. Some cancers had invaded the neurovascular bundle, so curative options involve necessarily amputation to get good margins. In cancer there's less doubt in the prognosis, so less chance of legal ramifications.
Your see this in coding agents too. The only times so far I’ve really seen Opus tie itself into a knot is where I’ve asked it to fix something that I thought was broken but actually wasn’t in the way I had described. It will bias towards your description (I’m guessing because that’s the most recent context it has?).
I feel like the promise of these models is to help people make more informed decisions. Improving the knowledge economy and general understanding.
The problem is these are just statistical models at the end of the day, so you need to know something to be able to identify the errors. You can’t let them really be autonomous and you also can’t really have people turn into glorified approvers. If the machine is correct 89% of the time, you cannot make people responsible for that 11%. It’ll just cause automation fatigue.
tl;dr: the actual use cases of these LLM (or generative AI in general) is rather limited, so it is offensive how much hay has been given to them eating the entire capitalist system. They are not fit for purpose.
Why should we not expect a computer vision model to outperform humans on reading medical images?
The human experts are literally just a trained biological neural network. In this domain they are not capable of anything a computer can't already do.
i'm sorry, but AIs only "know" about stuff that they have been trained on.
If we would allow AIs to be trained on the petabytes of medical data hidden in hospital systems, they would most likely be much better at diagnosing illnesses and conditions than the average doctor.
(Justifiable) Privacy around medical records so far prevents this.
You think you're cheering for humans, but in fact you are gatekeeping healthcare.
I dunno... if we gave an AI all of these medical records as training data, wouldn't it be trained to give the same answers as the doctors already gave, without knowing whether those diagnoses were correct or not?
Except it would see all the times similar starting conditions led to different diagnosis and recognize those contradictions. Or all the different treatments and their outcomes. And it would never forget or have bias.
It would be like the sum of all medical professors in existence.
Radiologist who does read shoulder MRI would like to add that over half the annotations are wrong, glaring mistakes in anatomy and cardinal direction which begs the question of how is it making these findings without knowing what it’s looking at (here’s a hint, it’s hallucinated based on reports it sees).
Agreed. Not a radiologist, but I do a fair bit of MRI research. Experts vs lay people probably have different success with getting the right diangosis out of a frontier model. Subtle changes in prompts can cause different diagnosis[1]
[1] https://www.nature.com/articles/s41591-026-04501-8
Huh, I'm reading and looking up these words you guys are saying and it is starting to look exactly like the symptoms I have been having with my own right shoulder! I feel like a giant gaping rabbit hole just opened up next to my desk.
We're discussing calcific tendinitis (https://radiopaedia.org/articles/calcific-tendinitis?lang=us). If you think you have it, you can see a doctor and consider shoulder radiographs to start.
If you think you have it, then you don't. If you have it, you won't think, you'll know.
Spoiler: because it hurts like hell.
Why isn’t diagnostic ultrasound used in orthopedics? They inspect fetus hearts and other organs everyday, why not shoulders? Seems much cheaper and faster.
They do. Ultrasound in orthopedics is a relatively newer field, and there aren't quite as many sonography techs and radiologists experienced in reading these studies, which is likely why you don't see it offered more widely.
Edit: I should mention that ultrasound is basically unusable for evaluating bones. Sound waves can't penetrate bone, and so you end up just seeing a huge black void. That's a huge orthopedics use case that ultrasound just can't benefit. However, ultrasound is fantastic for evaluating muscles, ligaments, tendons, and other superficial soft tissues.
Serious question: If the bones specifically show up as black on ultrasound but the surrounding (muscle, etc) don't, wouldn't that be an option that could be used to try to determine a broken/fractured bone without the radiation from an xray? Or are the gaps in those cases usually too small to pick up?
We order ultrasounds all the time for shoulders (for like soft tissue issues; for trauma, you'd start with an xray). For other joints, such as the knee, MRIs are a better choice (unless htere has been substantial trauma, in which case xray initially or further), though more expensive, unless you're excluding a Baker's cyst, in which case an ultrasound is fine.
Since MRIs are more expensive, private doctor's might order them instead of an ultrasounds.
(I'm a doctor)
Where are you? Pi and work comp attorney in medium US midwest metro. I've never seen one in 20y. Not from HCA ERs, medicaid er visits to univ affiliated er, nor prestige practices.
Ultrasound was overlooked by US medicine as a first line imaging tool for a long time because it takes real skill and experience to do it right. But it's making a comeback. We've had Chinese, Indian, Australian, and American doctors visit us for one to two month stints to build up their skills.
Given the skill involved, it's probably a liability concern they don't want the exposure over there.
They're used quite a bit for nerve entrapment—both in diagnosing and treating.
It's a manual, non-standardized process without a standardized output. Image quality depends both on user skills (how deeply they press the sensor on the skin) and the machine they have. Unlike CT/MRI the examination results cannot be easily shared and compared between patients for studies.
> I'm a radiologist
Any comment that doesn't start with this or similar qulaification should be taken with a grain of salt (yes, including this one).
Medical imaging is one of those things everyone thinks is simple because they don't know what they don't know. I'm a cardiac sonographer, and I have to assume radiologists hear at least as many eye-rolling takes on AI coming for their job as I do.
Ahh, AI is coming for your job.
Full sarcasm, is there one that’s that’s more immune?
I don't completely understand what you mean, but I can tell you for my job, having AI tell you how to get the images is (without exaggeration) like putting someone who's never played an instrument on stage and saying "don't worry, the AI will show you how to do it."
I did a lot of cardiac MR and often GA cases. Sometimes after the scan an echo would be done.
I know my anatomy and etc and have done a short stint in ultrasound. I have no idea what you are doing or looking at and can identify pretty much nothing.
Echo techs are going to be around a lot longer than MR techs.
cough Immunology
I mean, probably not. No expert, but everytime I go to an immunology meeting (I'm a paediatrician) they've got a whole stack of new diseases. The field is moving fast, and there has to be a careful amount of shared decision making about when to test, what a positive test means and so on. I reckon they're as safe as any of us.
yeah, you said "one that is more immune"
So Opus might be correct?
Does radiology really make +$700,000.00 a year ?
Someone on reddit claiming to be a radiologist claimed that.
I wonder where the savings will go when those jobs are gone.
> Does radiology really make +$700,000.00 a year ?
The radiologist I know does not, but they are paid very well (and these numbers are always dumb when you're not sure if they're living in Manhattan vs literally anywhere in Kentucky)
Like most medicine, a large % of the job could be done by any decently talented person willing to follow instructions and shadow for a few months.
Like most medicine, the remaining % is what you're paying for, because it is literally life and death and you can't do things like "pull the logs" or "lets turn it off and take it apart" or "huh i need to put this down and come back later". Even in radiology, because "well lets just do it again to be sure" is often not a viable option.
While there is a problem in how we have inflated the cost of education for medical fields, the insane health insurance issues (US obviously, but it does have some effect globally when the expert radiologist you hire from the US to help with research costs that much), and probably some better ways to approach splitting the work for the entire field, like most professions dealing in life or death, medicine likely will always be paid well.
Physicians salaries account for about 8% of healthcare costs in the US.
You know the radiologist you're responding to is a real person? Your last line seems needlessly callous.
The savings go straight into patients' worse outcomes.
To the consumer! Haha just kidding. We all know where they'll go.
It funny to see the community here expects the human body to be treated like a deterministic function: for input X expect output Y - and that transfers to diagnosis - people expect to receive the same diagnosis from different specialists for the same issue.
Given human body complexity, the diagnosis is a compound output of the experience, knowledge gained throughout the career and diagnosis methods/equipment, the title (like Dr) is a certification imposed by the state so its "safe" to let people practice since they passed "the bar" - but that doesn't imply everyone will be treating the same.
Some specialists update their knowledge monthly, some yearly and some don't do it at all, there are so many variables in play here (geo, politics, even weather haha).
Having said that, choosing the specialist is really important, getting opinions about their practice and their speciality, you can only maximize your chance of getting the right diagnosis, but don't expect to get it right just because somebody is called a Dr.
> It funny to see the community here expects the human body to be treated like a deterministic function
In a community largely made of people whose job it is to produce such functions, I'd say it's to be expected
I'm not sure what your point is. Are you saying that medicine is inherently fallible and therefore AI is more likely to make a good diagnosis - particularly a cluster of specialist AIs?
Yeah I think the OP is muddling the point by conflating "physician's version of the diagnosis" with "The Diagnosis".
There is absolutely one "The Diagnosis". Human body is a machine, albeit a very complex one, and all measurement sources have noise. But they are all measuring one reality, and if there is a problem, there should be one explanation that all measurements align with. They can be noisy but can never be conflicting (instrument error notwithstanding).
Physicians' ability to arrive at "The Diagnosis" would vary, but it does not mean one does not exist. I am not sure if characterizing human body as derministic or not is relevant here.
I think „the diagnosis” is over simplification and lots of professionals would disagree that there’s always a single one. As a patient your goal is to eliminate the symptoms of whatever is going on in your system. Often times there could be many reasons for it and only curing one can help you already. The diagnosis is a help tool to choose the roght curation method.
Thus, chasing the „right” diagnosis (whatever that is?) is pointless, as it only the outcome (reducing symptoms, stopping the damage) can tell you if the diagnosis was right, but not the only one right.
> I think „the diagnosis” is over simplification and lots of professionals would disagree that there’s always a single one.
"The Diagnosis" does not mean "one root cause".
Situation: my car has some unexplained vibrations. 1. Mechanic A says that it is the engine mounts 2. Mechanic B says that it is some weirdness in how the exhaust assembly is hanging to the underbody 3. Mechanic C says that it is just my wife farting
I replace engine mounts and 40% of the problem is reduced. I then drive without my wife and the remaining 60% is solved.
"The Diagnosis" was: 40% mounts, 60% wife, 0% exhaust.
There is always one "The Diagnosis".
> There is always one "The Diagnosis".
No, that is not true at all.
This is a kind of thinking a lot of programmers fall prey to. The real world, outside of code, is a very fuzzy and inherently analog place. There is very rarely one in any complex system having a complex problem needing a complex solution. At some point even the definition of diagnosis gets fuzzy.
The best demonstration of this in medicine is probably the DSM-5. What, really, is the difference between Narcissistic Personality Disorder and Borderline Personality Disorder and Generalized Anxiety Disorder? Can they overlap? (Yes.) How do you treat them? (It's not easy.) What about depression: how do you tell if someone has Major Depressive Disorder or Bipolar Depression? (Again: not easy.) In some circumstances the only way to tell the difference between the two is what drugs work: if antidepressants help, it's Major Depression; if mood stabilizers help, it's Bipolar Depression. It's kind of odd to define a One True Diagnosis by "well we fixed it this way, so it must have been that", with no other way to do it, isn't it? (What if both work? What if one works for a while, then the other works? What if treatment with antidepressants induces bipolar (hypo)mania? All of those happen!)
And that's just a few examples.
Pyschiatry gets complicated because the failures are not mechanical. Even if you could image every single neuron in a person's head we do not have a very good way to define an algorithm for these issues. I do not have a good answer for psychiatry.
> This is a kind of thinking a lot of programmers fall prey to. The real world, outside of code, is a very fuzzy and inherently analog place.
Having said that, I would vehemently reject and push back against this, and without doubting your sincerity, characterize it as an ad hominem.
The vast majority of issues with the human body are mechanical in nature. Restricted blood flow, unwanted tissue, a broken bone, a bad valve etc. These are causal descriptions of "disease". Where causal descriptions exist, the "One True Diagnosis" principle holds. Psychiatry just happens to be unique in that it is a fuzzy science where we rely on checklists and ultimately all diagnosis is probabilistic.
EDIT:
> This is a kind of thinking a lot of programmers fall prey to. The real world, outside of code, is a very fuzzy and inherently analog place. There is very rarely one in any complex system having a complex problem needing a complex solution. At some point even the definition of diagnosis gets fuzzy.
I would also push back against this mindset in general. This is not a falsifiable claim, it is incoherence as an argument, and I do not need to be a programmer to hold this position.
That the real world is analog is irrelevant to its amenability to causal explanations. Or "fuzzy": "fuzzy" in this context just does not mean anything.
I am not trying to sound exasperated or win internet points, just impress this point on you and anyone reading this. We can write math to predict weather, make it tractable to solve using approximations, tolerate IEEE 754 weirdness, and finally tell what the clouds will do a week from now. This is nature telling us that there is a pattern to how it behaves, and it is the only weapon we have as scientists.
To say that nature is not amenable to explanations is a very defeatist thing to say: neither Newton nor Einstein nor any of the million-odd people that have built modern society would exist if nature did not have causal explanations. I urge you to reject this defeatist thinking.
There's quite a few diseases and conditions that don't have definitive tests. For example, alzheimer's and parkinsons are diagnosed based on medical history and symptoms. With alzheimer's an autopsy can tell for sure but that's not much help for a patient. I'm sure there's other things out there with similar situations. Hard to come up with "the one true" diagnosis with an definitive way to determine it.
> With alzheimer's an autopsy can tell for sure but that's not much help for a patient.
Ok let us unpack this statement.
For your point to hold, I would have to be saying "all kinds of practical diagnostics are invented now. No progress can be made in better diagnostics".
If Alzheimer's can be validated by slicing open a dead patient, there is a causal mechanical explanation for the disease. If we can not confirm that defect without slicing open the patient, that is a limitation of 2026 tools. The "One True Diagnosis" is an Oracle explanation that all real diagnostic techniques try to approach in the asymptotic sense, and it is helpful exactly because it clarifies in discussions like this.
There are going to be diseases where we do not yet have causal explanations. Or where we treat them without establishing them. Hypertension is one example: while technically it can be caused by vascular stiffness, some weirdness with the RAAS system, some hyperadrenergic weirdness, practically you get a lot of mileage out of just prescribing people telmisartan if they're old.
That does not mean the frontier of hypertension is settled, or the 10% who do not have a vascular stiffness problem would not benefit from better causal models of hypertension. Science is us continuously pushing back against the fog: of the tools we have in 2026, some are great, some are imperfect, some are promising etc.
There might be "one true diagnosis" but there's no reason to believe that we'll have practical diagnostic tools to get it. If we need to sample the brain chemistry to diagnose a neurochemical disorder, it's probably not too useful in a clinical setting. The world makes no guarantees that we will be able to differentiate between certain situations with tools that we can realistically access and build.
Today's limits are known and undisputable. Tomorrow's limits are a promise: some promises over-deliver, others under-deliver. :)
Regardless, to bring the discussion back to the claim at hand: at all points in future, we will need the ability to reason under partial information. "Absolutely flawlessly complete diagnostics" is an asymptotic goal we get closer to but never reach. This is both very doable for a disciplined human, and very hard to outsource completely to an LLM. Treated as tools operatored by competent users, they are magical. But they can not outperform their user.
I've seen a lot of friends and family members almost immediately get offered surgery for shoulder pain. It's just often the default for people that do surgeries for a living.
I also had a pretty painful shoulder issue at one point, where the pain just wasn't subsiding for months. I tried massages and acupuncture as I didn't want to do surgery, but it wasn't helping at all. The thing that fixed it for me was just really focusing on doing pull-ups. I couldn't do them at all when I started, so I began with dead hangs and scapular pull-ups, eventually progressing to regular pull-ups, and then training with a "grease-the-groove" method once I could get a few per set. I stopped the training schedule once I was getting in around 17 pull-ups per set, and now just do 6 sets of about 7-8 pullups 3x per week spaced throughout the day. I'll also do some shoulder mobility drills [1].
Whenever I get lazy about keeping up with them inevitably discomfort will start arising again, but it goes away once I get back to strengthening.
[1] https://www.youtube.com/watch?v=vP8YmmRMz6I
I had issues with my shoulder for years. Tried PT as well as pull/push-ups but doing that made the pain worse (if I wasn't doing any exercises involving the shoulder it was "fine")…
same here. I started doing yoga and rock climbing, and it stretched everything out, and strengthened all the muscles around it. I rarely have an issue now.
On the flip side, when I had rotator cuff issues, the surgeon recommended months of physiotherapy before resorting to the knife. And it worked. And by weight training regularly with a focus on correct shoulder movement, the pain stays away.
It really seems like if you, as a patient, go looking for a quick fix, that’s what you’ll be offered. And if you educate yourself a bit and then go t for the best fix for you, you usually get they.
Physical therapy is very often under recommended in the US under the belief that insurance won’t cover it. They might. And, for anyone reading, you don’t even need a referral for the first 30 days in some states. Physical therapy is for more than just hip replacements and car accident trauma. Like regular therapy, a lot of “normal” people can benefit from it. It’s also not just stretching.
As somebody in the US who had to do 2 months of PT before I could even get an MRI of an injury, this is both surprising, and yet also not, to hear.
I broadly agree though; about a decade ago I had the standard office worker low back pain problems which cleared right up after doing squats multiple times a week. Of course a decade later I managed to blow out a disc at the gym, which I still work through as I write this today, but well worth the risk in the long run. Even with that long experience of strength training, the PT was worth it even if it didn’t fix my problem entirely. It added some variety and pointed out some details I had overlooked to improve my shoulder health.
Interesting. I’ve never not had some PT coverage. The copays kinda suck, but major surgery tends to add up as well, so…
What did you have exactly?
With calcifications, physio without the shockwave component definitely doesn't allow going back to the normal gym routine. It's just not enough.
Garden variety inflammation with some minor tearing, exacerbated by weakness/instability.
Strengthening with PT kept the joint stable enough to stop rubbing and allow the inflammation to clear.
And as long as I stick to a regular gym routine that includes rotator cuff work, it doesn’t recur (and did the few times I lapsed).
But absolutely, PT doesn’t fix everything. Bit for a lot of things, it’s worth trying - but it might also means a lifetime of altered habits to keep whatever injury/problem from recurring.
~2 years ago I used ChatGPT "deep research" to investigate a chronic sinus infection I'd been fighting for ~3 years. After seeing 3 GPs and 3 visits with an ENT, I fed all the observations I had into the AI. In particular, I couldn't get the ENT to explain why he visually saw, via a scope, evidence of allergic reaction in my sinuses, but then later concluded, after an allergy test, that it couldn't be treated via allergy medication. I asked this question a few times and he just never answered.
ChatGPT surfaced a NIH study that concluded that 20% of people have allergic reactions that are isolated to a body location, and that shoulder "skin prick" testing may not reveal. I asked him about that and he said "that's not how allergies work". Full stop. He was unwilling to even look at the study.
He prescribed a CPAP and regular nebulizer treatments. Side story: the CPAP place sent me a SMS message that I couldn't recognize was not a phishing attempt, and when I reached out to inquire who they were they never replied.
So I decided: Let me just try taking a second-gen allergy tablet every day and see what happens.
My sinus infections have gone away. Previously I was getting a major sinus infection at least quarterly. Maybe he's right that allergies don't work that way, but allergy tablets have absolutely solved my problem. Which I'm thankful for because I tried a CPAP for a solid month a few years ago and I just could not get used to it, and was sleeping like crap.
Daily allergy tablets are associated with huge increases in early onset Alzheimer’s. Glad you found something that works, but might be good to get some of the allergen injections :)
That seems to be only for first generation, drowsy-making, tablets. Second gen formulas don't cross over the blood/brain barrier.
https://www.myalzteam.com/resources/zyrtec-and-alzheimers-me...
There IS one year-old finding that suddenly stopping Zyrtec after daily 3-month use may lead to nasty itching, and if that happens you can re-start and then taper off. https://www.fda.gov/drugs/drug-safety-communications/fda-req...
Zyrtec/cetirizine is a weird one. Everyone seems to agree that it is a second-generation antihistamine. The FDA seems to play along. But there is no particular shortage of anecdotal evidence of people who find it quite sedating (hi there!), there are some papers questioning its status [0], and the FAA puts it in a category with diphenhydramine, not with loratadine and fexofenadine [1].
[0] https://pmc.ncbi.nlm.nih.gov/articles/PMC1118461/
[1] https://www.faa.gov/ame_guide/media/AllergyAntihistamineImmu...
Where are getting that from?
All I can find is about 1st gen antihistamines (i.e. Benadryl, which I doubt many people take daily, because of the drowsiness).
Even for those, evidence seems to be mixed at best. "Huge increases" seems like hyperbole.
Only first gen, 2nd gen does not have this issue anymore or it’s greatly reduced
Misinformation.
Only first-generation antihistamines with anticholinergic effects are associated with cognitive decline in elderly patients.
LMAO at how the two of you sound authoritative and knowledgeable, but neither linked to ANY studies (or at least personal anecdotes) to support your claims.
Yet here we are, warning each other about the dangers of LLM hallucinations. Humans "hallucinate" (provide random authoritative-looking information without anything to back it up) pretty often too.
I believe it depends on which ones, the older gen or certain classes of antihistamines
Wait, what?? Now I'm getting in panic mode because I do take regularly anti-hystaminic tablets/pills (the newer ones, based on ebastine because they don't make me feel sleepy)
You don't seem to need to worry, second gen tablets seem to be fine: https://claude.ai/share/9c6eaaa5-e734-4267-b540-ddc0188daf6b
Posting a claude chat is not actually helpful. The chat doesnt't even cite any sources.
I think this post is a decent summary, the answer is a soft maybe: https://www.health.harvard.edu/mind-and-mood/should-i-worry-...
Second-gen tablets might increase dementia risk by a small-to-medium amount (there's almost certainly still a small degree of CNS activity, and we don't know what causes dementia in the first place), but researching it will be difficult. Dementia is hard to research because of how long it takes to develop, and it's poor coding within health data, and antihistamines are hard to research because they're not often prescribed and aren't available in the health data.
If it's a large effect, those factors wouldn't matter, but smaller risks are harder to detect and more sensitive to bias. If you want to minimise dementia risk, then reducing antihistamine use might be warranted, but you're probably better off addressing the risk factors we do know about: https://www.dementia.org.au/brain-health/risk-factors-develo...
>Posting a claude chat is not actually helpful. The chat doesnt't even cite any sources.
You didn't read it very closely, I posted it specifically BECAUSE it cites sources (14 of them by my count).
edit: I sit corrected, if I open it in Incognito mode the citations are removed. That's not very useful.
edit 2: Here is a gist with the citations: https://gist.github.com/linsomniac/6d2bdeb0f63cf504354b067e2...
Ok, there's a lot to unpack here and you really had the deck stacked against you. First, lets go from the top, once a test says X, disproving that X is really hard. And that's not unique to the medical profession, it's inherent to all humans and we suck at revisiting or revising our decisions, much less at looking at the possibility to even reverse it.
Which moves us to the next two issues: liability and time. Any moment that you ask someone to revise a decision and specially with the stakes that the medical profession has that nobody has the time nor the inclination to open themselves for a mess.
Now, if you really want to be successful, you have to, before they even have a case with you, and specially before the diagnostic loop closes, to suggest the tests that the study has, since that has the biggest chances of looking at the right thing to look. Just be straight that you walked in with a theory. Doctors notice when they're being steered way faster than they notice when you're actually right. That's how you work with the systems that have a overworked mass trying their best.
>before they even have a case with you
My problem is that I needed information from 2 ENT visits to feed into ChatGPT to get that study. On the first visit he scoped my sinuses and immediately said "I can see evidence of allergic reaction, see those white bumps?". On the second visit I got an allergy stick test and it came out negative.
Those helped lead to that NIH study. It would have been very hard to have walked in with that study in hand.
I mean, for the second (third?) option specifically. You will notice that they will be more open to go outside of the standard of care if you have a working theory.
I don’t understand the negative reactions. Medical care as it exists requires the doctor and patient to have their brains switched on. I’ve almost never had a problem where a doctor provides me with a diagnosis and I go about my day. Most of the times that I have, I’ve been confident about the problem and known what I needed. The doctor was a barrier to accessing care.
Dr. GPT is a good brainstorming tool. It helps synthesize information in a way that primary texts don’t. But it does force you to say “that doesn’t make sense”.
I do think that people saying “doctors don’t know the state of the art” have a weaker case. If you think about it in terms of token density during pretraining and how post training datasets are constructed, I think it would take us a very long time to adapt to any fundamental shifts. If we have forgotten how to cure scurvy, how many journal articles would it take before we adapt to a discovery?
As a radiologist I have found Claude and ChatGPT to be absolutely terrible at MRI and I would not trust it one bit. It has its merits if you need to research stuff that is more text based, but radiological images is just something that they cannot interpret good enough (yet)
AI makes up for its poor reporting by enhancing the images.
Current Siemens MR software ‘Deep Resolve’ makes up the signal (adding about 50%), then makes up every second pixel, and then, for 3D sequences, makes up every second slice. It’s locking about 59% of the time off each sequences. And it’s really really good. I’m an MR tech.
but those are two different things. Of course something like Deep Resolve is great, as are modern model based reconstruction algorithms for CTs, but here we are talking about LLMs and their ability to interpret medical images, which has nothing to do with what you said.
Sorry? You use AI to hallucinate medical images and that's good?
It is not really the same as LLMs. I wouldn't call it AI. And I wouldn't say "makes up". I work in this field and this is certainly based also in part on my research.
‘Makes up’ is inaccurate for sure. But it’s not strictly true to call it acquired data either.
After years of collecting artifacts and errors, I have more and more respect for the tool.
But it’s jarring. I open a sequence, decrease the acquired resolution, add the AI and get a scan that’s quicker and higher resolution.
It’s an amazing time to be an MR tech.
It is amazing. It is the result of two decades of research in image reconstruction algorithms. The machine learning is part of it, but that it is sold as "AI" has probably more to do with marketing.
I haven't seen it marketed as "AI" by GE, Siemens or Philips. They usually gesture at "deep learning" or "compressed sensing".
No radiologist is buying "AI" scanners. Radiologists are probably among the most jaded of an audience about the word "AI" due to decades of undelivered promises. AI is synonymous with "worthless trash" to them, not to mention everyone says "AI" is going to put them out of work. lol
It certainly has a lot of marketing behind it.
https://marketing.webassets.siemens-healthineers.com/2861d15...
Super-resolution is certainly distinct from hallucinating - it just rearranged data that was already there to make it easier for the human eye to see - but should be used with care. I can easily imagine that an upscaling algorithm makes it so a certain defect is clearly not present, when the source image is ambiguous (which the radiologist would have noticed), and in reality the defect is present.
Most upscaling and super-resolution techniques I’ve seen use various implementations of interpolation; typically nearest-neighbor approaches. Although I don’t work in the medical field and haven’t checked in on the research at least since ViTs overtook CNNs for other areas of computer vision.
It's just DLSS/Frame Generation for MRI's.
Sure but claude and ChatGPT are not Siemens 'Deep resolve'.
It's like people who expect ChatGPT to be really good at chess because chess engines with super-human performance have been around for decades, so obviously the latest frontier LLM that took billions to train should find the task trivial.
Actually, I'm curious what ChatGPT 5.5's ELO is- I wouldn't be too surprised if it's 2000+ just from its basic understanding of chess principles from all the content it has digested.
ChatGPT is completely unplayable at chess on its own. It's unable to keep track of the state of the chess position and therefore will make an illegal move within about 10-12 moves. I would put GPT-5.5's rating at 400, since it can't even make legal moves reliably.
I've tried to pay chess with GPT-5.5, even played it again tonight, allowing it to use `python-chess` to keep track of the state of the position and to get a list of legal moves at each turn, so that it was fair. I also gave it blindfold odds, again to make it a fair fight, but it was not even close. GPT still isn't better than maybe 1000 Elo, maybe 1200 tops. Even with what amounts to being able to see the position and also being unable to make an illegal move, GPT-5.5 hangs material left and right, doesn't make a plan, and got smoked even when I gave it blindfold odds, to the point it's boring for me to play even under those conditions. I'm not sure it's better than whatever the GPT model was that was out about 8 months ago. I also thought it might be somewhat better than a beginner due to reading chess books, but no, it's complete garbage at playing chess, not even average-level skill.
Interestingly LLMs are extremely bad at chess position _images_. I have to imagine if you give it positions in text it'd be pretty great but when I was learning chess and pasting images of positions in for analysis I couldn't believe how wrong it was. I actually thought it was looking at the board in reverse but even when pointing out problems it seemed completely incapable of understanding what it was missing (of course... it doesn't really "understand" anything).
LLMs truly are marvels with text but anything spatial seems to really mess it up, somehow.
> I have to imagine if you give it positions in text it'd be pretty great
Not at all? LLMs are a terrible match for the kind of analysis a chess engine does (scaled deep search, deeply trained position evaluations). It's just not that kind of tool.
I suppose that's also a good point!
That might be doctors new nightmare: people who second guess everything with AI. Previously it was "google your symptoms".
Well I live in the nightmare that is the Dutch healthcare system [1]. There are many things that they will fix but they didn’t fix my sleep. A friend fixed my sleep. He is a doctor and prescribed me the right thing. The thing is, he shouldn’t have had to intervene. Without him I could have ended up poor and destitute as my sleep was wrecking me.
And yea, I already did all the standard things. CBT for insomnia helped somewhat. My insurance didn’t fully cover it either, unless I was willing to wait for 8 to 12 months.
And I recently met someone with slow moving metastatic cancer. Thanks to LLMs they will most likely live another 3 to 5 years extra since the Dutch conventional mainline treatment hasn’t been taken yet. But it is German doctors that helped them and Belgian doctors that pointed out in a second opinion that a lot more can be done.
LLMs have a part to play. The false positives are awful, but I have seen an average of 5 out of 10 care when things become too complicated.
Except for trauma treatment. The Dutch healthcare system is amazing once they diagnose classic PTSD.
So it’s definitely not all bad but the trust I had when I was younger has been eroded quite a bit and LLMs can meaningfully step in, in my case at least.
[1] I know there are worse systems. But from what I have heard there are clearly better systems nowadays. It has slipped a lot
Hey what did you do to fix your sleep? Help us all and maybe an llm will index your diagnosis (hi ChatGPT)
For me what helped is taking 7.5 mg of mirtazapine. At higher levels it's an anti-depressant but at lower levels it's an anti-histamine. It gets me drowsy. Together with 0.3 mg melatonin it knocks me out. I only take it 3 times per week max to not have habituation kick in.
So 3 days out of 7 days I have guaranteed good sleep. The other 4 days are a toss up. But an average of 5 days of good sleep is much better than 3.5 days out of 7 days.
Interesting. There recently was an article about how premenopausal and menopausal women are taking antihistamines with pepcid to help them sleep due to it going viral on tiktok.
https://www.thecut.com/article/antihistamines-pepcid-ac-peri...
> Then, a few months ago, Angela saw a social-media post from a woman who took daily anti-histamines (like Allegra, Claritin, or Zyrtec) plus Pepcid AC (a common antacid) for her perimenopause symptoms. Her results, as reported, sounded miraculous: no more brain fog, no more tossing and turning all night. Even her mood vastly improved.
AFAIK mirtazapine shouldn't cause habituation the way actual "sleeping pills" or benzodiazepines do. That's one of the reasons it may be preferable as a sleeping aid, especially in the longer term.
Anecdotally, when I took mirtazapine for sleeping problems, it did sometimes seem to have a stronger effect the first time I took it after not using it for a while. After that the effect stayed stable. Overall it shouldn't cause habituation, and my doctor said as much.
Of course trust your doctor and not strangers on the internet, though.
Is the dutch healthcare system broadly against hypnotics? Culture (of the country or its medical system) can massively influence prescriptions or their lack thereof e.g. france is pretty famous for prescribing hypnotics very easily (and having a broad range of them), while the UK is generally a lot more reluctant.
yeah, I’m surprised Trazodone didn’t get mentioned as a very low dose
The NYT did this profile a while back: "Ben Riley was already writing about the risks of chatbots when his dad started trusting A.I. over his doctor."
The dad was a retired neuroscientist who delayed cancer treatment against medical advice because he was certain he had been misdiagnosed based on his own research that he did with the help of A.I.
https://www.nytimes.com/2026/04/13/well/ai-chatbots-cancer.h...
There's a comment on the article from Ben Riley:
> I am very grateful to Teddy Rosenbluth for sharing my father's story with the world, her kindness and curiousity proved to be restorative in ways I didn't anticipate.
> The two words that everyone used to describe my dad: "intelligent" and "kind," and he was indeed both of those things. The sad irony here is that it was his human intelligence, combined with these strange new tools that purport to be a form of 'artificial' intelligence, that led to his ill-advised decision to forego the treatment he needed for his CLL. A doctor has already commented on this story with the observation that AI "confidently asserts erroneous conclusions," and we simply have no idea how often this is happening or the magnitude of the harm that results.
> Not a day goes by that I don't feel the pang of my father's absence. He might still be here if not for AI. I try not to think about that, but sometimes I can't help myself.
The context is very important: decades of a poorly-diagnosed chronic illness had left him deeply distrustful of the medical system.
This is the real root issue.
At 75 years old, he was stubborn. Is that reasonable ? Yes, perfectly. Could he have been right since the beginning ? Certainly. Did he deny evidence ? Yes.
Zero doubt that he was intelligent, everything points toward that direction, but that doesn't make a person less stubborn, because accepting the evidence, is also accepting that you were wrong if you initially postured yourself as adversarial instead of cooperative.
He would have read Wikipedia, scientific papers, etc, even without AI.
He did not want to be convinced. It works both ways:
https://www.foxnews.com/health/woman-says-chatgpt-saved-her-...
or
https://www.today.com/health/mom-chatgpt-diagnosis-pain-rcna...
Nonetheless, someone very smart, just didn't want to move from his position.
i mean, other smart people have famously delayed cancer treatment without needing poor guidance from LLMs! that's not at all new or unique to LLM chatbots
GPT-4o, which is what that article is most likely about, was an older low param count slop model which was known for abusing emojis and sycophancy. It does not really have any relevance to latest claude frontier models.
Your comment is akin to saying "Karen from facebook who is a human pushed essential oils and ivermectin as a cure to cancer. Now doctor Y is suggesting chemo. Both are humans, humans cannot be trusted!"
It's not just the second-guessing. It's the getting in the ballpark but striking out: explaining in detail why they are not correct. A little bit of patient knowledge requires a tremendous amount of doctor time to explain away the ignorance.
It's a 180 for me: While I believe doctors should explain diagnosis or treatment decisions when asked, I don't believe they should be taxed with explaining away alternatives. In my anecdotal 2nd- and 3rd-hand experience, doing that is taking at least a third of their time (on roughly 5% of the patients who think demanding answers will make things better) -- with zero improvement to diagnostic accuracy or treatment effectiveness. Doctors already consult with other doctors, and it makes no sense for them to have to consult with ignorant patients or treat their AI psychosis on top of their disease. It doesn't increase patient autonomy any more than adding a steering wheel for child car seats would help toddlers learn to drive.
I asked a clanker about symptoms I was having. (I'm not an idiot, I was already on my way to hospital, clanker was just to take my mind off symptoms during the drive.)
The clanker said I'd be fine, I just needed some rest and OTC meds.
The medical staff immediately turfed me to surgery because the same set of symptoms I told the clanker were enough to concern them that I needed emergency surgery.
Had I have listened to the clanker, I'd be dead because I did need emergency surgery. (Hell, I almost kicked the bucket because I waited for someone to wake up to give me a lift because.my insurance probably doesnt cover an ambulance ride.)
Very curious what made you run to the emergency first thing in the morning that an LLM understood as "just normal, take some OTC meds and wait".
Not OP but wanted to note that you're also likely to get different results based on the language you use (it'll respond differently to dialects of English, for example) and the RNG seed of the current session. These things are still probability engines and even if you know the exact symptoms, this might not be reproducible
It’s funny every profession deals with customers making their own guesses at diagnosis.
I told my mechanic the film flam is broken but he said it was the rim ram. He fixed it and we all went in with our lives.
But doctors insist on this God like status so it’s a “nightmare” when patients try to help themselves.
I dunno man, it's one thing to have your car still be broken because you were wrong, it's a different thing poison yourself on the basis of having done your own research. The mechanic can laugh at you, it hits a doctor differently.
you are literally taking sleeping pills ..
Nightmare because they're always right and the A.I second guessing is always wrong, or because they just don't like to be second guessed?
There’s more than two options here. It was already difficult to deal with self diagnosis for doctors, now we have a machine that outputs recommendations, and does it with confidence whether it’s correct or not.
The same issues that were present with search-engine self diagnosis are still present with LLMs. If you provide Google with an incomplete list of symptoms and can’t interpret the information you find correctly, you will likely get an incorrect diagnosis. The same is true for LLM output.
The A.I is only gonna get better , and fast. Doctors should simply double check themselves by using A.I.
Everyone on the internet loves to put doctors on a pedestal, but I think upwards of 30% of my doctor visits have been misdiagnosed.
There's a reason I ask AI about absolutely everything medical and there's a reason I keep extra quantities of prescription medications around for emergencies. I've saved my own ass a lot more times than the doctors have, thanks to good doctors not being available.
There are quite a few disclaimers everywhere that soften confidence: "always ask a medical specialist", "I'm not a doctor", "this could have been this or that but really not sure", etc.
No one cares about this, especially those who believe the machine. It's just there for the provider to avoid responsibility.
Well it was a nightmare for my mother's do-nothing GP surgery in the UK. She had several conditions which were being handled completely separately without central coordination, and her health was in serious decline. We went in with a list of 20 AI-generated questions based on her conditions and treatment (which I was able to screen as I have a bio postgrad, but not medical training), including those related to NICE guidelines and procedure, and, frankly the GP bricked it and ordered a load of new interventions. My mother started to get proper treatment.
I wouldn't trust AI to make a diagnosis, but I would absolutely trust it to notice where procedure hasn't been correctly followed, where a treatment is counter-indicated because someone has missed a line on a health record, or where there's a clear potential alternate diagnosis which has been missed for spurious reasons. Also, unfortunately, where doctors aren't doing a decent job - often because they're overworked or underfunded.
Nightmare because users approach LLMs with the false confidence that they're always right, and present LLM outputs as fact to Doctors who have to waste time explaining that it's wrong most of the time. It hurts more than it helps.
Its a nightmare because it erodes trust. Doctors are not "always right" which is why "always get a second opinion" is codified in culture.
But AI's problem is that its completely full of shit, sometimes, and the people most qualified to evaluate whether its full of shit are the doctors, not the patients, but just like OP's original article, patients are left feeling like their second opinion from AI might be more trustworthy than their doctors opinion.
The notion that only doctors can verify is false! Doctors are better at verification but normal people can also verify. This is just empirically true.
Examples of things normal people can verify
- procedural errors that Claude can capture like some blatantly high dosage (grams instead of milligrams)
- outdated treatment plan, maybe there’s a credible new treatment plan that’s been used for years but the doctors were not updated
- literally being injected homeopathic drugs (takes no smart person to flag this)
Let’s stop talking as if doctors have a divine right here. And let’s accept some agency.
Nightmare because the AI is just generating a random text that fits the question.
This is not a fair assessment of what AI is doing.
Studies have found that newer reasoning AIs are about as good at diagnosing illness from a written description of symptoms as doctors are.
Granted, it cannot actually examine a patient, so we're not replacing doctors anytime soon. But your view is obsolete.
https://www.science.org/doi/10.1126/science.adz4433
They are using the “gold standard for the evaluation of expert medical computing systems” not a proxy for what a doctor actually does when diagnosing someone.
It may have some utility after diagnosis, but this test doesn’t demonstrate utility for patients.
I feel the same when visiting a doctor in Canada. In that 2 minutes I have with they in one appointment per year I hear a standard text.
Not quite. An LLM generates text that would likely follow. The sky is… “blue”. A patient in pain with a bone protruding from their shin has a… “broken leg”.
The more training data, the more questions it can answer with a reasonable degree of probability of accuracy.
Throwing away a potentially useful analysis just because it’s probabilistic seems a bit like throwing the baby out with the bath water.
This is a very peculiar use of the word "random".
Indeed. I don’t even get what OP thinks they are getting out of this other than doubt.
This is obviously going to happen. But sub-par and sloppy doctors are a thing too. Medicine has been using semi-intelligent systems for years that were nevertheless found to improve outcomes.
We need studies that quantify error rates from each source type, then we need to account for the fact that the artificial type will keep improving.
It can be helpful in your understanding the choices made by asking questions and thus in reassurance, but it requires something most people lack: understanding you are likely wrong since you are just collecting information without understanding it.
Pretty much the like most manager these days, so I understand the frustration of the GPs.
People should've googled their symptoms and especially the prescriptions they got. It has always been a good practice. If[0] AI proves to be the new google then people should ask AI too.
[0]: IF.
Do you know how many life threatening illnesses I’ve diagnosed myself with by googling symptoms?
And say it's true because the AI said so.
No, this flow is actually very good.
Like any domain, when you have questions or need a solution, you make research first, then you ask a specialist.
If you explain well the symptoms and context you can have proper advices and then decide on the path next:
Once you have challenged LLMs, and read about the topics over and over then you genuinely become really good at understanding it (especially if you triangulate over LLMs and ask them to challenge, you start to have genuine questions). No matter if the answer is right or wrong, you have elements. Maybe you missed the point, but you come prepared.At home you have the time to assess the options, pros and cons of each approaches, the possible questions to ask and then challenge the doctor.
Shared decision-making is an actual evidence-based model of care, and patients who arrive understanding their condition and carrying specific questions tend to get better attention and better outcomes.
Some doctors get annoyed, because they have big ego and choose to be patronizing, but it is exactly their job to answer such questions.
The other problem, in many places: + you can have all of these factors together.So, you have something deeply bothering you, your only appointment is in 4 months. It would be insane not to take the time to explore different solutions and not to come informed about the topic.
If you express your prompt properly and do not rely on imagery, you can absolutely have top-tier advices.
Agreed. This gets worse in cultures in which Doctors have no habit or haven't been trained that educating the patient is part of the job. Whenever I am back to my birth country, I specifically avoid doctors that are older than mid 30s, because they all have the same, terrible bed manner. They might be good at diagnosing and treating, but they never, ever explain anything, even when asked. Some even have "helpful pamphlets" to hand to the patient - anything to avoid explaining. It seems that in their view their job is not helping the patient, but completing a task - running a scan, performing a procedure, administering medicine etc. The human, that is subject of the task, is invisible.
It's so much worse than some Google results: people see LLMs as a trusted friend who never talks back and never questions you, who is excellent at convincingly communicating their bs, reeling you in with "tell me more so I can really lock this down", continuing to fool you
A con artist, a fraud
I would not trust AI on images. But I once had ChatGPT tell me that an MRI report was very likely to be incorrect based on the text, and offered a different diagnosis. Since it was semi insisting, I visited another doctor who made me do a retest. Long story short, ChatGPT was correct.
Again, this is just one single person's experience. So not worth much.
I think that much of the visual gap is because what to attend to in images is less structured. Anecdotally small qwen finetunes (ie less than 10B) take task accuracy from sub 30% on FMs to 90%. We have sold some of these for outcome based back office tasks.
I think we’ll see a lot of specialized VLMs that provide real value.
Anecdote but I gave Gemini Pro an image of an individual with Herpes Zoster which the doctor said was something else. Gemini gave the correct diagnosis which allowed for correct treatment and cure.
I don't understand why doctors don't prompt LLMs before saying wrong things. Is it ego?
I can understand for radiology because you need a specialized convolutional network, but for more knowledge based things...
“A man with a watch knows the time; a man with two watches is never sure.”
I imagine reasons for what you’re asking might include:
* Prompting an LLM is work, and they’re already overworked just doctoring—every conversation with a computer is a conversation you’re not having with a patient;
* They’re probably right more often than they’re wrong;
* “When you hear hooves, think horses, not zebras”: the 15th case today of strep throat is probably strep throat, regardless of today’s 15th falsely-confident LLM weighing-up;
* They tend to have spent many many years honing a clinical intuition that makes an examination, to some degree, hard to articulate fully to the LLM;
* Liability/overdiagnosis: All this stuff is probabilistic. Inevitably, there’s going to be a time when the LLM throws out something I thought unlikely that turns out to be right, and there will be other times when it’s wrong but now I have to document why. How many false leads do I need to chase per one true differential? Does this really compare favorably to seeking a second opinion from another human doctor?
* Not everything needs to make it into the record. Once it’s in the LLM, it’s discoverable and litigable and hackable and permanent;
* Medicine is practiced in very different ways in different contexts—even in this thread, one radiologist routinely orders ultrasounds for soft tissue shoulder problems, and the other medical-world person replying has never heard of such a thing—presumably both within US health care contexts. Some doctors hand out antibiotics like candy, others are more cautious with respect to resistance. What’s right can depend on the time, the place, the clinical setting—more than just the immediate patient-level facts at hand, in ways that become awkward or unwise to express explicitly.
And of course… who’s to say they don’t do LLM-assisted research, in cases where they think it might be helpful?
> I don't understand why doctors don't prompt LLMs before saying wrong things. Is it ego?
Either that or laziness I'd imagine. This isn't limited to LLMs. Expert digital assistant systems that you query have existed for a long time. A good physician will double check anything even slightly unexpected against one.
mate the other day chatGPT (enterprise) told me that the kernel 7.0.2 was older than 6.69
you cant trust these toys at all. that doesn't make the useless, just untrustworthy.
I feel like I'm going nuts.
There are other commenters saying this is a good practice they've also done for other injuries. You are saying you are an actual radiologist and immediately clock the problems with its advice.
I have seen this pattern over and over again. Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading. It is only when you do not know what the AI is being asked to do is it likely you will find the output helpful.
This is itself alarming to me, but no one else seems to find this to be quite damning for the AI services being offered, preferring instanced to be wowed by the convenience and speed at which they can be delivered unreviewed and unproven information.
(We detached this subthread from https://news.ycombinator.com/item?id=48709121.)
This is the root of AI psychosis. There’s a lot of unpack here, and I won’t go too deep because you can’t really have a discussion with affected folks because their fundamental basis is not evidence, it’s belief.
It is weirdly religious in a way, because if you were to present contrary evidence (e.g. experts in a field weighing in about how plausible sounding responses are bunk), you would only be told you don’t believe enough in the long term potential and capabilities.
Don’t get me wrong, I think we all agree capabilities will eventually improve (and farther-future capabilities could reasonably surpass experts), but really is unclear if the current transformer architectures with their probabilistic/hallucinatory outputs will plateau before they surpass current experts abilities in all promised fields.
I was a very early adopter in my circles with AI and I shared it with many people. Strangely, I seem to be the most skeptical about AI in my circles as well, but because I was the gateway for a many folks, they want to come back and share their experiences with me.
And it's so much like listening to someone in a church congregation sharing their experiences with god. Clear and obvious gaps are hand-waved away exactly how you're describing.
>This is the root of AI psychosis. There’s a lot of unpack here, and I won’t go too deep because you can’t really have a discussion with affected folks because their fundamental basis is not evidence, it’s belief. Treating it as if it is an intelligence is the problem.
The problem is that AI psychosis is fundamentally the belief that an LLM is "thinking" at all. Outputs are just believable word vomit which resembles factual information.
That presumes that we have a definition of "thinking" or that we know that anything is "thinking" when in fact neither is true.
The problem is real but I don't think positing a philosophical root is helpful
The claim that we are assigning human-like agency to a machine with none is simple and factual.
What's "thinking"? What's "agency"? What's "human-like agency"?
If "agency" is making decisions and performing corresponding actions in the real world, then LLMs most definitely LOOK LIKE they're making decisions (what's the next token? which tool to use? what's to say, in general? what idea to convey?) and performing actions (tool use). Can we tell whether they are ACTUALLY making decisions? Well, are the people around me "actually" making decisions? Or are they simply pushed around by circumstances and external forces?
Am I actually making decisions? Did I like DECIDE to write this comment? Maybe? I have no clue...
I think you're mildly obfuscating the issues at hand by diving too deeply into philosophical questions.
It's quite simple, the agency that the LLM appears to have is actually your own. Without a prompt an LLM does nothing. It has no thoughts between prompts about you or your problems.
Yes, I'm diving a bit too deeply because I don't really know what "thinking" is and therefore I don't understand how we can so confidently say that LLMs don't think, even though they definitely LOOK like they're thinking. They even have a "Thinking" section in their responses! If I say that a rock doesn't think, it's pretty convincing: does a rock look like it's thinking? No — it doesn't even do anything! But an LLM does look like it's thinking, at least while generating a response. When it's "offline" it's just a bunch of "dead" bytes, sure.
So when it's not active, not responding to a prompt, it's of course not thinking. I'm pretty sure nobody actually questions this. Is your computer "thinking" when it's powered off? Can a piece of metal think? Probably not. So there are no thoughts between prompts, this seems obvious.
Thus, this is a question of "discrete time vs continuous time". LLMs "live" from prompt to prompt. Humans are alive continuously. In some sense, we're prompted by a lot of things all the time. As I'm writing this, I'm seeing stuff, I'm hearing stuff, I can feel various parts of my body, I'm thinking about my problems, my goals, other people's problems and goals, etc. When I'm in a sensory deprivation tank, my brain keeps "entertaining" me by "self-prompting", like a recurrent neural network (I guess it literally is a massive RNN).
So it seems like your definition of "thinking" hinges upon the LLMs being discrete-time and single-threaded (can't think about multiple things in parallel).
IMO a more interesting question is whether an LLM is thinking WHILE IT'S GENERATING A RESPONSE, while it's "alive".
I want to say I really appreciate that you are putting a lot of thought into this, you certainly have interesting concepts here. However I think it seems a bit far off from the discussion I'm trying to have, and I do not have the bandwidth to fully understand and charitably respond to your points.
We don't know what thinking is but pattern matching is definitely a big part of it. That's why people see Jesus on a piece of burnt toast.
You are implying definitions that don't seem to be mainstream; thinking is internally manipulating information to reason, infer, plan, solve problems, and form judgments or beliefs. Also -- "Without a prompt an LLM does nothing. It has no thoughts between prompts about you or your problems." it sounds like you paint this like it's something fundamental? It isn't. Nothing is stopping you from streaming information to an LLM and letting it process this information, this is precisely what people are trying to build.
The machines have no driving force to act in the world. That is fundamental for humans.
Twice in your comment you suggest things that you think that I believe, please do not do this.
“It sounds like you believe” is a question, inviting your clarification. I will continue doing that because it’s perfectly reasonable. Also “machines have no driving force to act in the world” is also a mysterious statement but because you reacted so badly to anyone questioning you I will just leave it at that
That is called a leading question and it is not "perfectly reasonable." Resisting your attempts at bad faith discussion is not "reacting badly." I agree though that we should cease discussion.
Wait, where are we assigning human-like agency in this case? Agency to me means the ability to do something by itself. Here the LLM is not doing anything, it is just responding with information to queries from people, that those people may then act on. (Which you can say about Google searches too, yet we don't ascribe agency to Google.)
The idea that humans have agency is supernatural thinking imo
A free will versus determinism argument doesn't really have a place here. Consider instead that humans factually have 'the illusion of agency.' The LLM does not even that have that. It cannot act on it's own, it has no ongoing drama or intention. It only reacts to prompts.
You're confusing the training method with the internal process. If I had you repeatedly attempt to learn how to make believable completions of partial documents about a given topic, you would eventually learn things about that topic and could use your knowledge to create more believable completions of documents about that topic.
LLMs do not learn. You put it out to pasture and create a new one. "Memory" in a session is essentially a context window party trick.
They already learned. A lot or basically everything evern written and available digital.
And context window work very well. You can 'teach' an llm a new programming lanuage and other things through it.
They do learn in context, and very sample efficiently. Continual learning is active area of research and we sort of already have something resembling it with persistent context. So yes they do learn.
I consider that to be the illusion of learning. You are not wrong, I think they may actually learn in the future though. But not today.
That’s strange to me, what would you define as learning?
To acquire new knowledge and build your understanding. They don’t understand so they can’t learn
Thank you for saying succinctly what I could not. If your consciousness and knowledge fundamentally does not change from your ongoing experience, then you are not learning. This is how the LLM currently functions.
You’re describing the problem of continual learning. As I said their “consciousness” for lack of a better term and knowledge does already change from ongoing experience in context which is another of saying for only a short window, today. They are ephemeral, sort of, but that’s a temporary limitation.
I think if your definition of consciousness can fit these things then you’re more open minded than I care to be. Consciousness isn’t really guessing the next thing to say- it’s hard to say what it is, obviously, but blindly feeling forwards with each new conversation doesn’t seem like consciousness or learning to me.
We aren't talking about consciousness, we're talking about learning.
> Consciousness isn’t really guessing the next thing to say-
I don't know what consciousness is either and these debates are a dumpster fire when they happen, but it sounds like you're pulling forward this "LLMs are just predicting the next token" (true by construction) implies that they can't learn or reason or be conscious (2/3 are wrong, the last one isn't falsifiable without a useful definition).
“They don’t understand” is a strong statement, maybe true but depends on what you mean by understand. What is your definition of this? I can’t think of a meaningful definition of “understand” that doesn’t apply to LLMs
We’re in all the same threads lately!
They learn during training, which is what we're talking about.
>which is what we're talking about.
You are anyway, I don't see anyone up the chain saying that.
The LLM itself doesn't, but agents can research, compare, add to their memory, and use that to narrow the results down to a probabilistically higher set of outputs; I have used an LLM for my own MRI results and it was nearly spot-on, verified by a subsequent visit to a specialist. YMMV as they say. But I do believe we are entering the era where LLMs are considering past interactions and long context windows to inform it of personal preferences and history in order to output more accurate results.
believable != true
A very important callout. It's the crux of the whole thing really. Humans are easily susceptible to deception by statements that are structured to be believable.
This is what Stephen Colbert called "truthiness". People want to believe what they feel is true even if it is directly contradicted by evidence.
https://en.wikipedia.org/wiki/Truthiness
Sure. But that's not the subject.
Please stop trying to police what the subject is to suit your own arguments.
Often times the words produced do have legitimate factual information though. It's less psychosis and more a confluence of well known human tendencies - salience bias, automation bias, etc.
The big problem is often times they don't as well. That's why we can't rely on them.
Same with humans? Doctors, scientists...if a tool has any error rate above zero its not reliable?
I don’t think they will improve, there is too much incentive to poison the datasets going forward.
A lot of the models up to this point have been benefitted - like Google did - from essentially ‘pre SEO’ internet.
Now the same tools are being used to generate nigh infinite good sounding bullshit, which poisons the dataset in all sorts of hard to detect ways.
To add insult to injury, the human experts are also not as. Naive, and have many incentives to poison their own input in subtle ways too.
I seriously doubt that data set poisoning will be a real limiter in model performance.
For one, if your website/book is poisoned, who is going to trust it for anything at all, much less for training models?
For two, all the major AI labs hire or contract for subject matter experts to create curated data sets, evaluate model performance, etc.
Unless they hire malicious experts, this will provide a growing, high quality data set that should drown out any poisoned pretraining data.
There's a post every other month where some dude who put nonsense information online celebrates because it actually ended up in some frontier models weights.
If it's easy enough that some randos can do it for fun, what do you think happens when there's commercial interest behind it?
Obviously companies are going try nudging AI towards recommending whatever they're selling. It's a logical extension of SEO - and that's a 100 billion USD industry.
Additionally, if I believed myself to be in some sort of spending - err - AI race, I'd try to poison the data sets of my competitors by putting crap out there for others to ingest.
It's not really a problem. We're out of natural tokens anyway. The future is synthetic verifiable traces (already the way we train coding agents).
> synthetic verifiable traces
What does it mean, Is it like when somebody used some coding agent to develop a feature and later input prompts and a resulting PR can be used for training by a presumption that final PR was a correct implementation of a prompt?
Yea it’s rejection sampling, so you have an agent, you take a verifiable problem (people use lots of different verification signals but say unit tests etc) and have the agent attempt it K times. You accept the trajectories (all context, tool use etc, the entire log) that are positively verified and use these as training examples.
The trick is to find the examples that are just in between too difficult and too easy for the existing agent, these have the strongest training signals
There are so many better data sources that AI labs can use here that this argument really holds no water at all.
Peer reviewed journals, textbooks, in-house teams of experts, trusted news publications, etc.
The whole idea of scraping large swaths of the internet for training data has always been pretty dubious due to the variable data quality.
I mean, just look at the early Google models that told people to put glue in their pizza due to a joke in the training set. Garbage in, garbage out.
This is one of the first and most obvious problems all of these labs have run into, and countermeasures are only going to improve.
But they don’t, generally. Which is why it is a great argument, because it’s easy to falsify - and see it is what is actually happening.
Also, those other sources are getting buried in AI slop too.
The question is not whether it has happened or will continue to happen. Of course it will always be a problem to some extent.
Your original claim is that this will be enough of a problem to prevent models from improving in expert level knowledge. I completely disagree with this premise.
If the models fail to improve, it will likely be due to limitations in the transformer architecture rather than poisoned training data.
And even then, I doubt that the transformer is the best architecture we will ever come up with.
Clearly it doesn’t learn or think like a human does, since humans don’t need many gigabytes of text samples to learn to talk, so there is some room for improvement.
https://arstechnica.com/science/2025/01/its-remarkably-easy-...
Great, an article about Llama 2 from early 2025. That doesn’t at all invalidate what I said.
While completely ignoring the fundamental reason. Whoosh.
Not sure what point you’re trying to make.
Do you have examples of such celebrations?
They already are, It has become a real problem in Reddit. Especially with the latest in pseudo-science crap like peptides.
I think you underestimate just how much money is being poured into LLM SEO at the moment. It's real quiet because they don't want to draw attention and countermeasures from the frontier labs, but this is getting huge investment, and they will have a monomaniac focus on juicing product results whereas the attention of the labs necessarily has to be spread out.
Data curation is important and expensive and frontier labs can afford to do it right. Natural data isn't the limitation, we are already literally out of tokens. It doesn't matter how much you poison things it's not going to stop the progress train.
Who's doing llm seo right now? How does that work when you only gets feedback every few months when a new model is out?
I'm pretty sure the Optimization part is just ... not present at all.
This is how we get LLM summaries presenting something mentioned once by some nutjob in a reddit thread as bona fide FACT
Look at G2.com - they found their website is highly references by AIs and they are leaning into it hard.
Pretty easy to display one thing to verified browsers (just latest few user-agents from the 10ish different mainstream browsers on the 3 main OSes) and another to anything else.
Yes AI scrapers can easily spoof user-agent, but they fall out of date as the browser updates.
Bit harder to catch them in tarpits and then serve nonsense to whoever ever triggered the tarpit.
>Yes AI scrapers can easily spoof user-agent, but they fall out of date as the browser updates.
It’s a hell of a lot easier for a company to ensure that its scrapers all report the latest user agent string than it is to get everyone and their mother to update their browsers in a timely fashion.
yeah but unless everyone is checking the version, if it's just a handful of websites checking it, they don't.
and browsers forcibly auto-update
Human doctors use LLMs to diagnose too
OpenEvidence claims
https://www.cnbc.com/2026/01/21/openevidence-chatgpt-for-doc...This is a very misleading statement; most of those physicians are using LLMs to transcribe notes from visits and/or for billing purposes (e.g., proper billing codes).
The problems isnt LLMs per se, it is the shift to trusting the output of the machine coupled with a decline in verifying that the output is reasonable. It's basically what your teachers warned you about with wikipedia in eight grade except applied to all areas of life, including medicine. Dictation is already high-stakes and LLMs do not automatically reduce that risk.
Here is an example. My provider sent me this note. I'm quoting verbatim here from my MyChart record:
"Your liver enzymes are high, I would like to order acetaminophen containing medication like Tylenol, I would like to order liver ultrasound I placed ultrasound order in the system, make an appointment for radiology, I would like you to get hepatitis panel lab work done, obtain blood work order, please schedule a well visit to get it done"
When I queried it, this is what I got back. It was a dictation error. You could almost hear the panic in the message:
"Sorry for wrong message earlier, I was dictated message- so could not realize that it was written to take Tylenol type of medicines- I DO NOT RECOMMEND ACETAMINOPHEN CONTAINING MEDICINE - LIKE TYLENOL AND ALCOHOL DUE TO ELEVATED LIVER ENZYMES."
Again the problem is not dictation, or LLMs. The problem is humans ignoring their responsibility to check the output of a machine.
> Again the problem is not dictation, or LLMs. The problem is humans ignoring their responsibility to check the output of a machine.
100%. Also, management.
I wish someone would go ahead and coin an AI version of Amdahl's law that states the work speedup from AI is dependent on amount of unverified AI output used.
Iow, if you 1:1 verified everything, there would be no time savings.
Ergo, you get management saying (1) we demand time savings due to AI & (2) we demand you fully check anything you use AI for.
End result? People skip (2) to hit (1).
Then management burns anyone at the stake whenever inevitable mistakes happen.
But that’s trivially false. There is an entire category of work where it is hard to come up with an answer and easy to verify the answer, which means that if you verified everything there would still be a large time savings.
I would question whether that holds in the practical LLM automation space.
Can you think of any real life examples where an LLM is likely to be used?
I think in practice what you're saying is there are problems where there exist efficient deterministic verification methods, and I'm sure that's true.
But that's not the bulk of everyday work LLMs are being asked to do nowadays across industry.
Which is itself a problem as (in my partners evaluations as an optometrist), LLMs used for clinical notes has a bad habit of dropping clinically important information, and the biggest providers don’t give you a copy of the raw transcript or a recording
Which means she ends up spending just as much time as if she’d done it herself as it needs to be verified for accuracy every time…
OpenEvidence is specifically meant to help clinicians make evidence-based decisions in the diagnosis and treatment of patients, not note transcription.
It does both: https://www.openevidence.com/user-guide/visits-overview
Ignoring the fact that this number comes from a company press release, it doesn’t say anything about the number of doctors using it to diagnose, just that they use it.
If a physician uses Google to search for a dosage chart for some drug they rarely prescribe, you wouldn’t say they are using Google to diagnose the patient. You wouldn’t say that either if they used Google to search for the most recent studies on a topic.
To me this is like a good software engineer using AI.
The fact that they use it doesn't make what the result is any worse or less trustworthy - arguably it makes it better.
It only becomes a problem if they offload all of the thinking to AI.
Human expertise is also improving all the time and not limited to just connecting dots. When AI seems to surpass a particular human, it's just because the human lacks broader knowledge and fails to investigate further.
An expert already knows they don't know everything. That was never the point. Critical thinking cannot be delegated to AI any more than it can be delegated to a book. There is nothing new going on here.
> There’s a lot of unpack here, and I won’t go too deep because you can’t really have a discussion with affected folks
Do you think it is any more possible to have a proper discussion with someone who preemptively paints the other person as mentally ill? Or someone who preemptively victimizes themselves?
Cause I don't think these are the hallmarks of an honest discussion. See also the entire past decade of political discourse.
Like, consider this:
> It is weirdly religious in a way, because if you were to present contrary evidence (e.g. experts in a field weighing in about how plausible sounding responses are bunk), you would only be told you don’t believe enough in the long term potential and capabilities.
A trivial counter to this is that you can just be an expert at something (e.g. your own work), use the damn thing yourself (professionally), and evaluate the outcomes for yourself. Then maybe remark "LLM good".
Now you come and remark "LLM bad", and point at random "evidence", either of outright other workloads, or even the one at hand: you're asking someone to reject the reality they've already experienced, entirely based on the assumption that they're "merely religious" or "in psychosis". You tell me if that's any more epistemically rigorous and sensible than their story.
Why is it psychosis and not lower standards?
While I can understand being skeptical of non-experts' claims that such answers are enough, I don't understand why you call it "psychosis" and not simply naivety or lack of expertise.
At the same time, the new so-called "models" haven't been pure transformer-based LLMs, but entire systems with tools (with access to the Internet), data storage, and the options to trigger additional instances for different tasks.
Because some people develop actual psychosis. They go down some rabbit hole with an LLM until the LLM makes them believe they invented new kind of physics that makes them go harassing experts who obviously try to ignore them because its all nonsense.
For me, what others said and literally showed with Claude Code, et al, and what I’ve been experiencing with it, clearly signal way lower standards. But this was true even before LLMs.
Reminds me of that clip of Travis Kalanick, sexual deviant and harasser of women, talking about "discovering new physics."
The Uber guy? Yeah that was a painful watch.
Graciously diagnosed for them by random unqualified people on the internet with an agenda, frequently before even any relevant interaction:
"Oh you like LLMs? You must in AI psychosis!"
Let's not pretend it is anything more than the run of the mill wet fart of a culture war label. It's quite literally the "TDS" of the anti-AI crowd.
That's really not the argument being made here, and you're panning it further by claiming this is staunchly anti-LLM.
The idea here is to signal that you can absolutely use LLMs to help you figure something out. But also, they're wrong a lot. So use your own brain too.
Totally agree. I'm a scientist, and like most scientists I have some specialized skills that most of my colleages don't. AI has empowered them to learn and build things that they might have otherwise needed me for. But there have been quite a few cases where it led them very far down a wrong path. This has started happening way more often in the last few months.*
We've known since the beginning that AIs confidently say incorrect things. But now that they can speak confidently about very complex topics, and mostly say correct things, we are letting our guard down and lots of subtle falsehoods are slipping through.
*In one case, I was able to put things back on track because the AI suggested my colleague talk to me; somehow it figured out we were co-workers.
Right but hallucination rates have been consistently decreasing every model iteration. It's about error rates. As also a fellow scientist, I also will mess something up. Humans have an error rate. Once that error rate is low enough, it doesn't matter that it's > 0, it matters that it's low enough to be trustworthy and useful. Coding agents of 2024-25 had error rates too large; you couldn't meaningfully vibe code anything and needed a ton of oversight. It's still true but FAR less so, and this is after like a year of iteration.
>very far down the wrong path.
Absolutely agree. Have seen this first hand
I see your argument, but it's not exactly news that an expert found a flaw in a popular tool. You could say the same about Wikipedia--experts have tons of issues with it, but Wikipedia still provides value to non-experts. The most likely alternative to Wikipedia for non-experts is simply not trying to learn anything new.
Similarly with LLMs, you can't just write them off entirely because they sometimes provide misleading or incorrect advice. The positive utility maximizing view is to learn when you need to call in an expert. I recently moved in to a new house and have used Claude extensively to figure out basic things (e.g., adjusting the garage door height, how to mount a TV). However, when the HVAC suddenly stopped working, I gave Claude a shot for an hour and tried some non-destructive fixes, but then realized I had to call in an HVAC expert.
The free alternative to Wikipedia is the library, not “don’t learn anything new ever”.
I find Claude is surprisingly similar to a confident but incorrect coworker, with the benefit that Claude will reevaluate when I correct it.
I used the phrase "most likely alternative" intentionally. The library is where people should go to get answers in a world without Wikipedia, but the vast majority of people won't. So in practice, most non-experts either learn from Wikipedia or don't try to learn anything at all.
Sure, if we’re going to go that broad. People are already leaning heavily towards learning nothing instead of using Wikipedia.
I guess to me it has to be comparable to be an alternative.
Like, I don’t consider doomscrolling x an alternative to reading Wikipedia but I might consider it an alternative to CNN, even though they’re all technically and very broadly activities that I could use to inform myself.
In that same way I don’t consider the multitude of ways I could use my free will necessarily alternatives to each other even though they technically are. It kinda sucks but going that broad feels to me like it breaks the concept of alternative and makes it kind of meaningless.
I get what you're saying, but I'm not deciding what should and shouldn't count as an alternative to X. I'm trying to answer the counterfactual: how do people behave in an alternative world without Wikipedia but otherwise identical to our world?
Claude will do everything to retain you as a user, because that's one of their most important metrics.
Excellent point my colleague has the exact opposite incentive.
Slightly OT Nitpick: in regard to experts and Wikipedia, when doing a neuroscience-adjacent MSc, experts in the field actually directed me to Wikipedia as an excellent source for high-level neuroanatomy, including recent research, so I'm not sure your blanket description about experts and Wikipedia is correct.
You 100% can write them off entirely and go about your business as you previously had done. Ignoring the errors, it is very debatable whether there are even productivity gains beyond: human programmer or whatever is excited and cranked up to unsustainable degrees of activity and thinking to 'keep up' with what he thinks is an AI doing the work.
I'm seeing this fairly often and when it isn't garbage it's a capable person who has gotten inspired by their 'collaboration' in which the busywork is being done by a machine, but they're doing so much directing and correcting that it's not unlike what would happen if they got heavy into meth and went on a tear.
You absolutely can write them off entirely and decide for yourself what your comfort level of human-killing speed-freakism you want to pursue in your productivity. There's a long history of humans managing astonishing levels of productivity through self-destructive means. This is not even cheaper, once the 'first one's free' wears off: it's just a novel method of getting humans to burn themselves harder in the belief that they have a magic feather.
The ones who're really throwing themselves into the situation are the ones who'll burn out, but who aren't setting themselves up for atrophy and learned helplessness. Anyone who believes the technology lets them be a lazy manager just getting paid, is in for an unpleasant discovery.
> Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading
Yes, this is exactly so. AI is able to confidently sound plausible enough to convince laypersons or anyone who isn't very familiar with the subject matter, which is a big part of the mass-appeal "magic" of ChatGPT and other similar tools. It's like having a know-it-all friend (who also makes shit up to bridge their own knowledge gaps).
In many non-advanced non-specialized situations, AI is right enough to be at best useful or at worst not harmful (usually landing in the middle somewhere).
But speaking for myself, in areas where I consider myself quite proficient, I can very easily spot the subtle inconsistencies and naive conclusions that AI responses provide, and I have to guide/steer/correct it a lot to get good results when the subject matter is complex enough.
Last week I went to a highly-specialized tertiary clinic about further treatment for a rare medical condition that I was diagnosed and treated for as a child. The two very specialized doctors I met there confirmed a diagnostic mistake that a specialist had made ten years ago. The only reason I pursued a second opinion, ten years later, was because Google Gemini had explained to me that the specialist ten years ago had performed the wrong type of test for my condition.
Do these LLMs make mistakes? They sure do, I see it all the time. But they can also help people make breakthroughs.
And this isn't the only time that Gemini has helped me diagnose long-term health issues, either.
I am not advocating to trust anything they say blindly, but they can be a great place to form new hypotheses and learn the right terms to look for when you are unfamiliar with a subject.
Can you elaborate on how you use Gemini to diagnose long term health issues? Considering doing the same for myself, but I have no idea what is too much vs too little information, and generally the type of prompt engineering to do.
Some folks are not going to like what I am about to say, but what I do is write down as much information that I think may be relevant as possible, trying to avoid leading the witness with any of my preconceived ideas of what may be going on. At the end, I encourage them to ask me questions to get a more complete picture of what may be going on.
After a couple of rounds of that, a picture will start to emerge. The AI will make a few XYZ hypotheses of what may be going on, some of which will make more sense to you than others. This is when you can start searching some of those terms in places like pubmed.ncbi.nlm.nih.gov, including for example like diagnostic criteria for XYZ.
One of the ways I often use these AIs, not just in the context of finding possible diagnoses, is requesting them to make the case for and against hypothesis XYZ based on the data you have personally collected. Again, it's not about fully buying every thing that comes out of them, but it can help you consider angles or possibilities that did not occur to you, or that you had previously accepted/discarded without sufficient evidence. Think of them as that quirky acquaintance that knows a little bit about everything but sometimes misremembers, rather than as a god-like oracle.
And don't do all this in a single session/context. Start a new context every now and then, because otherwise it tends to go in circles as these AIs are biased towards agreeing with whatever it is you said most recently. Intentionally challenge yourself, re-evaluate the existing data from other perspectives.
Sometimes what you learn is not pleasant, but as more data becomes available, you learn to accept it. Good luck.
I may be missing something, but I think it's unclear that the parent poster here is necessarily actually contradicting anything the AI said. It may depend on the exact information the OP wrote to Claude and GPT. The full transcripts would be needed. (Though there is definitely a separate point that a doctor would generally better know all the right questions to ask, while current LLMs may be making certain assumptions.)
The LLM may have, from its "perspective", implicitly thought the OP was telling it that he had strong reason to believe there was no calcification and was not considering the bigger picture of possibly receiving an incomplete/poor assessment from the medical staff. In fact, the issue here may be the LLM overly trusting doctors vs. trusting its own expertise.
I dunno. I know a lot of software engineering experts. AI isn't always right, but neither are the people, and it's getting better and better.
Software is one domain where it excels because of structured training data and simulation environments, so I'm well aware it's better here than other areas.
Still there's somewhere balanced between saying every time it's "insufficient or incomplete or outright misleading" and "just trust AI". AI's a useful source of information/reasoning/research, but know you need to validate it's answers for important decisions.
> no one else seems to find this to be quite damning for the AI services being offered, preferring instanced to be wowed by the convenience and speed at which they can be delivered unreviewed and unproven information
"Be wowed by the convenience and speed", or merely "take advantage of the mere availability"? What most people find to be damning about expert advice is that they simply can't get it anywhere, at any cost that they can afford.
So if you want to do a surgery but you don’t see any surgeons around you ask a grocery butcher to have his way?
In certain circumstances, the answer is yes. If an airplane's pilots are incapacitated, do you simply give up and crash the plane because there are no other pilots on board? Or would you rather have someone on the ground try to coach a passenger into at least attempting to land the plane?
As long as that passenger didn’t have the fish.
Yes, I remember, I had lasagna.
That's an extreme edge case, which I don't think is in the context of the concerns in this thread.
The specific case doesn't matter--it's meant to make you think about the general question throughout this thread: when an expert isn't available, should non-experts use AI (or other tools) to help themselves? Sometimes the answer is yes because the potential benefits outweigh the potential harms (if any harms exist). But sometimes the answer is no because misleading/incorrect advice can cause a net harm.
But if the cases where AI use is a net positive are one in a million in medical situations? The argument is surely about the ratio, which many people here are arguing (from anecdote, would be interested to see a real study) is not in its favour, and the potential downsides - from both false positives and negatives - can be huge.
A passenger crashing the plane while trying to avoid a certain crash doesn’t make things any worse. An incompetent doctor trying to save you from certain death can make things so much worse. It’s all about weighing the best/worst outcome compared to where you are now.
I hate to break it to you but death is certain for everyone.
Properly emotionally processing this fact and your complete inability to do anything about it is called an "existential crisis" and if you haven't had one or several yet, you will.
I’m not sure what the “revelation” is? How is this related to what I said?
Putting that aside, your philosophy sounds shallow. Death is certain, but how long you have to live and the quality of that life are not predefined. An incompetent passenger-pilot trying to save you from a crash will at worst make no difference. But an incompetent doctor can teach you that death isn’t necessarily the worst outcome.
There are many healthy psychological ways to accept the certainty of eventual death. But the process is inevitably painful.
I think the different ways people accept death explains a lot of people's psychology, like how you can guess people's attachment styles or Freudian stage fixation. For instance, billionaires who pour all their money into anti-aging research clearly are not handling it well.
You can choose a) a calm, level-headed passenger who knows they aren't a pilot, or b) a calm, level-headed passenger who almost has their pilots license but has a medical condition that prevents them from admitting when they lack certain knowledge.
Who do you choose to be coached by an expert on the ground?
No thank you, I will ask Claude and then ask ChatGPT to challenge me, and do a couple of rounds like that.
The first: Has no clue about anything and therefore no useful knowledge and cannot challenge me
The second one: Is proven to willfully give wrong information and will make me do mistakes for sure.
The LLMs will do their best, even if imperfect, since they summarizes what appeared in books.
I prefer to be grounded on what Airbus / Boeing manuals, or on what pilots training book said, than two far more unreliable sources.
People, especially in medical crises, are desperate for answers that they often can't get because their clinicians don't know. The illusion of an all-knowing guru who sounds like their doctor and tells them ANYTHING is extremely alluring. If you're waiting to hear back from a doctor about test results (which these days probably showed up on your online account the moment they were completed) can be agonizing.
Ok for pain in your shoulder it might not, but how about a woman with a lump in her breast waiting for the mammogram interpretation? How about someone trying to understand disturbing lab results? People are also often pushed these days to move through visits with doctors at a breakneck speed, but the AI will "hear you out" all day.
Part of this is a problem with the AI, part of it a problem with our healthcare systems, and part of it is simply human nature. If you think that OpenAI, Anthropic, Google and the rest weren't aware of this going in you must have very little faith in the intelligence of their members. It's not hard to imagine the future of LLM's should involve a hell of a lot of liability on the companies running it, but for now it's the Wild West.
> but how about
Whatever scenario you come up with my answer is the same.
As an adult I’d like to be able to choose what tools I use to learn about my condition regardless of how well it works or even if it’s likely to mislead me.
There’s risk in every aspect of life and we can’t baby proof everything.
>choose what tools I use to learn about my condition regardless of how well it works or even if it’s likely to mislead me.
Even if it "works" so poorly that you're not actually learning about your condition?
If it's helping you learn about your condition then sure I agree. The issue here is that's not really the case, it's giving you the illusion that you're learning about your condition while feeding you hallucinations and half-truths at best. A recent look at medical advice from these things showed they're no better than a coin flip.
So if you MUST have answers that are at most random guesses, I'd suggest saving a few bucks and asking a coin before flipping it.
The companies are 100% aware, yes, and so they did make quite a few changes over the years.
Current trend is that the models will try to explicitly steer you towards "asking better questions from your medical provider", rather than providing diagnoses. They do also evaluate whether something can actually be established rather than just listen and nod along. And so the "you must have very little faith in the intelligence of their members" goes right back against these failure mode ideas.
Now of course, given a sufficiently desperate person, they can probably torture anything they want to hear out of these models. But so can they out of actual people, so that's kind of a high bar. When you get to the point where people are willfully misreading a given piece of text, bets tend to be rather off.
No, people don't even go to a butcher, they do it themselves if they can. See the countless stories about farmers and their inventiveness. Example: https://www.youtube.com/watch?v=KKaJhQBusH8
Seems natural enough. There will always be complexity and nuance that is missed by an AI model or person - the world is just super detailed. The more expertise you have the more you will be aware of that nuance. That doesn't mean the model or person is not useful as a starting point.
> I have seen this pattern over and over again. Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading. It is only when you do not know what the AI is being asked to do is it likely you will find the output helpful.
I always recommend people try asking LLMs a lot of questions on something they know first. Programmers should start by asking LLMs to work on a codebase they’re familiar with first.
You’re overstating the problem, though. Even for an expert the LLM will get a lot of things right and can be helpful under a watchful eye.
The real problem is knowing how to identify when it’s on the right track and when you need to correct it, because both cases are presented with the same tone and confidence.
An expert can better identify when the LLM output doesn’t sound plausible. Someone unfamiliar with the topic will think everything it says looks correct.
On the flip side of this problem, novel best practices lag the medical standard of care, other human failures like corruption and competing priorities notwithstanding.
For example, we had to advocate for certain practices during the birth of our first child that became routine during our second several years later.
So, neither side is guaranteed correct, doctor or citizen researcher (which did not include LLMs in my case, for the record). The truest answer is also the most useless one, applicable to all fields: it depends.
The real question is: if you embrace being a layman, whom do you trust more: LLMs/the internet or experts, like doctors? I think the answer is pretty clearly experts.
You shouldn’t expect frontier models to work on medical imaging. There is much more that goes into building a medical imaging product. First and foremost is data. Medical imaging datasets are not prevalent one the public internet at the scale necessary to have good performance on medical imaging tasks especially MRI. Also the labels are super noisy.
This is completely different than asking for general medical reasoning which is more derived from papers, public standards and textbooks.
Text exists at the right scale but images don’t.
The question is how far is AI off compared to the professional that we have access to. World best experts are not accessible to most of us. :(
You're not. This site was also bullish on using LLMs as therapists, which defeats the very point of them, and reflects a lack of knowledge on what exactly therapists do for people.
More on topic: if the article's author arrived at a definitively negative result would this have shown up on HN?
This is a serious issue for young people I think.
I have seen outputs that look good but the actual content is bad. If you’re inexperienced in a field you can’t see it because AI makes anything look right.
I have gotten very good results with AI but you can’t take the first answer at face value. You need to be suspicious and challenging until you tweak out the right answer over time.
Well that's part of the problem. AI is not accountable - if you take its advice and hurt yourself, who is responsible?
A real doctor is accountable.
They might both "know" a lot of things but implicitly the party who is accountable is going to be more trustworthy.
And I don't see that going away until AI companies must be licensed for application x and can lose their license / be sued if engaging in malpractice.
No, not anytime someone is an actual expert at anything, AI output appears insufficient. That is why experts in various fields use AI.
Then to say "Aha, but all of that is AI psychosis" makes obviously no sense: Why would we trust experts when they offer critique but not when they say "this is helpful"?
Overall: People are not insane. AI makes mistakes and, often, fails completely. AI also helps them do things better, quicker, increasingly so. The jaggedness of AI is confusing and real.
How many times have you seen an expert go "yeah these results are good consistently enough for a non expert to trust them without expert assistance"?
There is a huge difference between having a chance of a good result, which can be useful for experts able to filter out the bullshit, and consistent success. I would generate code as a helper, I would never allow a guy from marketing to merge unreviewed AI code.
> How many times have you seen an expert go "yeah these results are good consistently enough for a non expert to trust them without expert assistance"?
But see now we are talking about something else entirely than the claim that I found dubious, which was: "Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading."
Consistently good enough !== anytime insufficient
That's what I would like to call job security. When you know how to read what is wrong, you can easily catch the mistakes and correct it. AI gets you there faster by doing a lot of things right and you correct the mistakes.
I had a realization recently that the problem with "AI isn't consistently good enough" is that experience is probably not sufficiently distinguishable from the experience most non-experts have with computer systems all the time.
As an industry we've been promising people for decades that if they put all their data into our special softwares they can get all sorts of information back out that will make life easier for them, reveal new insights and otherwise improve their understanding. But the unspoken caveat has always been that you have to put the right data into the right places, in the right format, in the right way and then you have to ask the right questions, in the right syntax, with the right tools. And if you get any one of those parts wrong, you're not going to get the right answers (or possibly even any answer at all). How many people have had their excel worksheet that they (or someone else they asked/employed) built for some task that has been working fine for the last year suddenly stop working or start throwing out nonsense numbers because some input changed? Or how many people have experienced their system seemingly throw out meaningless garbage because daylight savings changed right at the moment the report was being run? Or spent months operating on wrong data because the person who wrote the query misplaced a parenthesis and the query was searching for "(foo AND bar) OR baz" and not "foo AND (bar OR baz)". For most people, the computer and the programs they use to do their jobs are magical black boxes that most of the time produce mostly the right answers and sometimes get things very very wrong with no indication of what has changed. Which is effectively the same experience they will have with an AI, but now instead of needing to figure out some arcane excel pivot table and VBA script, they can just dump some raw data and a "natural language" question into the AI.
And that's not counting the fact that their experience with looking information up online is about the same as well. How many absolutely confident wrong takes have you encountered online for things you're an expert in? How many of those wrong takes have come straight from supposedly trustworthy sources like news companies or even other people in the field?
For most people, using a computer has always come with the asterisk that you should always be aware that the source you're reading could be very wrong, that the output is only correct assuming all the inputs and all the parts processing that input are also correct and that everything you do should be accompanied by vetting by experts, whether those experts were software developers or domain experts. For most people the only thing that's changed with AI is that it's a one stop shop for their "probably directionally right, almost certainly wrong in the details" access to the digital oracles.
I’ve never seen an expert use AI in their field beyond the initial ‘oh interesting’ stage.
>I have seen this pattern over and over again. Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading
media is awash at the moment with experts chiming in to support AI, saying their fields are being revolutionized, etc.
it seems unsurprising to me that the laymen opinion would follow the loudest media trumpets.
Yes. The PM’s “with AI I know enough to be dangerous, haha” means “I’m actually dangerous and I don’t realize”
This is true in broader contexts too. Bunch of experts can't agree on something fundamental which is hard to prove/ disprove, and they have strong opinions on the topic.
AI is much worse.
AI is an expert in everything you are not.
I came here to post this as my experience. AI is magical when I apply it to something I know nothing about. It far exceeds my expectations every single time. I know nothing, but here is a report with animated graphics explaining exactly what I asked it to explain!
In fields where I'm an expert... it makes a lot of silly mistakes that are annoying and I feel like they would just cascade if I didn't correct them early. (I still think it's a net win, but... I watch it and it watches me, and we both do better work. I'd even apply the "magical" adjective when it does stuff I hate but know how to do, like edit Helm charts. What would normally be 20 minutes of me griping about YAML indentation is just a correct diff in seconds. I'll take it!)
So with that in mind, I tend to distrust output that I can't verify. If a doctor was recommending surgery and I thought the plan was too aggressive, I'd get a second opinion. I don't expect Claude Code to have much medical diagnostic ability, as that is really not what the model is trained for, and I know how it performs on work that it's trained and fine-tuned for. That is not to say the output is wrong and that it can't have diagnostic value, just that I personally wouldn't feel safe trusting it. Wrap up the same model with fine-tuning in the domain and a harness that reminds Claude to do a lot of sanity checks, perhaps with a human in the loop to guide it back onto the rails when it gets hyperfixated on something that doesn't matter? That could very much be a useful AI product.
> Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading.
The term for when the press "gets it wrong" is Gell-Mann Amnesia (https://en.wiktionary.org/wiki/Gell-Mann_Amnesia_effect).
In that case, when you have personal knowledge of the facts, or know the specific domain area, you can see where the reporter mixed things up.
AI is no different, it's just a bunch of matrix math substituting for "the reporter" regurgitating what it was previously told. So the Gell-Mann Amnesia effect would apply just the same. If you have domain knowledge, you immediately see where the AI got it wrong. When you do not have domain knowledge, you have less chance of seeing where the AI was wrong.
> I have seen this pattern over and over again. Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading.
AI isn't even the first instance of this phenomenon, news articles are like this as well.
https://en.wiktionary.org/wiki/Gell-Mann_Amnesia_effect
> I have seen this pattern over and over again. Anytime someone is an actual expert at anything, AI output appears insufficient or incomplete or outright misleading
AI assistant are industrializing the Gell-Mann amnesia effect.
TFA doesn’t actually state where the bit about shockwave therapy came from and it wasn’t the main point of the article. The concern was about being given useless therapies. The homeopathic analgesic is concerning, at least to me.
I.e. nothing this radiologist said was related to the LLM’s advice.
Your instinct is correct, and in a lot of cases it's true. However, I've heard from enough doctors by now (a cardiologist, psychiatrist, and epidemiologist/former physician) that they use medical LLMs and find them extremely helpful, mostly as a way to either bring up knowledge they'd forgotten about or as a way to learn something new and then verify it. I'm extremely skeptical about LLMs in general and the connection to Gell-Mann Amnesia is apt, but I wouldn't necessarily write them off completely like that. There are experts using the models that find them genuinely helpful in their field.
Probably this is the point, and it's a point that has been brought up a lot of times in the past, maybe less in recent times: you need to know the things you're applying an LLM to. In this way, you can keep the good outputs while having the expertise to discard the bad ones.
>AI output appears insufficient or incomplete or outright misleading
It has been like this since the rise of "AI". The only people enthusiastic about it are usually the ones hoping to make a profit in one way or another.
It's like reading news articles. Seems reasonable until you read an article about something you know, then you see how wrong they can be.
LLM is not necessarily an expert system. Once there are expert systems for law, healthcare, accounting, governance…
https://en.wikipedia.org/wiki/Expert_system
Didn't they try that in the 80s and 90s but discover the real world is too variable for that to work?
We're past the point of Gell-Mann amnesia. This is full blown Gell-Mann psychosis.
This is natural and even logically expected. It's just Gell-Mann amnesia in action. The world has more people spouting on things than it has people knowledgeable in said things.
Apply that to the Internet at large, and realize where LLMs got their training. They're basically ConfidentlyIncorrect personified.
> This is itself alarming to me, but no one else seems to find this to be quite damning for the AI services being offered, preferring instanced to be wowed by the convenience and speed at which they can be delivered unreviewed and unproven information.
Welcome to the club? This new awareness you've found over the true quality of LLM based GenAI output has been what "all the haters" have been mad about for-ever. That the output of LLMs are clearly defective, and merely have found a cute trick towards making humans think they're less defective than they are actually measured to be.
And the corresponding anger and frustration to push the risks of genai output out onto others, while also aggressively pushing it as a feature you should be using already. You're behind don't you know, and whatever other lie I have to tell to trick you into enough FOMO to pay me 200USD/mo so I can sell FOSS back to you.
An LLM can only output the mean next likely token, and then add a bunch of extra noise on top of that so it feels interesting and not repetitive. None of this is new, the problem is, 50% of humans are below the mean, but have no idea. So when an LLM tells them some lie: well, it sounds so helpful! It's impossible for someone who sounds this helpful to lie to me, liars never sound confident! It must be PERFECT! I'm gonna tell everyone how perfect it is. so the bottom 0-33% think LLMs are fantastic tools that make nearly 0 mistakes in comparison to the bottom 33%. 33-66%-ish aren't sure, some times it's great, but it will make that random mistake sometimes, but I can catch most (or all of them depending on ego). and the 66%+ are angry about how many people are getting tricked by something so obviously low quality, or are lucky enough to not have to care.
An LLM can only output the mean next likely token, and then add a bunch of extra noise on top of that so it feels interesting and not repetitive.
So when an LLM was asked to analyze the unit distance conjecture, it just spat out a bunch of average-or-random tokens that coincidentally happened to correspond to a valid proof that had eluded humans for decades?
what is happening is that the gap between what the experts and AI know is getting smaller each year. this year sure radiologists are mocking AI's ability to interpret MRI results, but they are a lot better at that this year than last. In five years perhaps radiologists will truly appreciate AI, but I am not holding my breath because radiologists are notoriously slow to adapt to changes in medical science compared to other specialists like anesthesiologists or surgeons
A coworker just spend a weekend with AI analysing his image data. No problem detected. The official diagnosis came later: he had a serious degenerative problem.
These models don't have curated image exams in their training data. Your can't trust them.
We provide a second opinion service with certified human radiologists, if anyone's interested: https://expert.med
I need this but for dentists.
> My hope is that in a couple of model generations, we'll trust AI to review MRIs the way we trust it to proofread our emails.
https://www.nature.com/articles/d41586-026-01947-1
I've started asking my doctors whether they use AI, and if they say yes look for another one.
That study seems to be confounding factors and rushing to a questionable conclusion.
A very plausible explanation for the adenoma detection rate to have gone down is simply that its prevalence went down among the population in the second three-month period.
This was not a randomized trial. Concluding that "AI usage degrades physicians' skills" is questionable at the very least.
There's a whole bunch of other studies on this topic, as well as metastudies, and from what I can tell the problem is real.
https://www.sciencedirect.com/science/article/pii/S245195882... (+ cf. its references)
I don’t even trust AI to proofread my emails.
You should always be getting a second or third opinion from real doctors for matters like surgeries, radiology, etc.
One doctor diagnosis + LLM is gonna throw you off. You need more datapoints.
In the US, this is standard advice. I note that the OP is in Germany. Maybe they do things differently, there.
In Germany we get zero-th opinion because you can't even get an appointment within the next 8 months.
And when you meet them, if you're unlucky they don't want to hear your medical history and want you out of the door in two minutes.
The OP describes getting injected with a homeopathic botanical formulation and receiving another type of therapy that wasn’t indicated for his condition.
I wonder if this person was going to a traditional doctor or if they were visiting some type of specialty clinic as a second opinion. For most conditions you can find specialty clinics that will prescribe and administer (and bill for) a lot of non-indicated treatments, but some patients like being in the care of doctors who take action and do things after being recommended more conservative treatments by primary doctors.
Yeah, one of the big problems with that is that Claude/ChatGPT doesn't perceive images the way humans do at all, so when you upload an image to them, it gets tokenized in some form. This is why most LLMs are really, really bad at spatial recognition for image editing purposes for example.
So, unless you can turn the image into a natively tokenized format like JSON or something that somehow accurately tokenizes what's on there, I would NOT trust Dr. Claude's analysis. If you want a second opinion, talk to another doctor. A human doctor.
This seems less a demonstration of competence than of accessibility.
Often we only get 10-15 minutes with the one health professional that makes a determination and sets the path of your life for some time.
As opposed to being able to spend hours with an LLM that in many ways feels more sympathetic and helpful - even though it's competence is in question.
So here we are:
1. with doctors processing patients every 10 minutes like a machine.
2. and machine's processing, for a patient, at any hour like a human.
The only part of the message I think it would be interesting to the author: what if you set two instances to prove each other arguments wrong considering that each reads one of the report as their POV?
I didnt see the full process but I used unet models for tumor detection so I am somewhat familiar with the possible caveats of any evaluation from a engineer perspective.
First, I would like to point that unfortunately, it is not uncommon to go to two different human doctors and also get two unreliable diagnosis and treatment. The biggest problem, in the way people plan to use ai on health is the lack of liability.
A bug on a regular old web site doesn't kill anyway nor cause pain and suffering (most of the times) but misdiagnosis + the fact that a model is very good on presenting arguments even when it is completely wrong.
Claude code, and I am talking about opus 4.8 here, can tell rivers of information about code pattern and develop the poopiest code the next line.
This is a machine that will deliver a sort of templates document based on the input information but it is not exactly doing the work if you don't directly it to do it right constantly.
Because the model isn't thinking I wonder what happens if you set multiple agents to communicate and defend their point with some sort of harsh penalty prompt for not fulfilling its goal. There are some safety system prompts on Claude models that will trigger it to be very carefully to write. Like: you cannot make mistakes. "You need to ensure that it is correct or someone might end up hurt or even dead"
But you would need two agents and a setup to communicate via pipes or files.
I was diagnosed with a rare blood disease called Essential Thrombocythemia (ET) which is part of a group of diseases called myeloproliferative neoplasms. This happened about three years ago. Recently, I decided to get a second opinion and my new specialist changed my diagnosis from ET to Polycythemia Vera (PV). She also highly recommended I quickly go and give blood to lower my haematocrit levels as it put me at a much higher risk of a blood clot. This is standard practice for people with PV but not people with ET. I decided to put the details into google AI in the same way that the original specialist used to diagnose me. Google AI predicted I very likely had PV instead of ET. I also asked Google AI how one could misdiagnose my condition with ET instead of PV and google correctly explained how. My specialist had used my high platelet count and blood test that came back with a JAK2 mutation then after a bone marrow biopsy to incorrectly diagnose me with ET. My high hemoglobin levels should of been checked by my first specialist as an indication of PV not ET. Only the second specialist picked up on this. Google AI took five seconds, and is free. The specialists costs $$$ and took weeks.
Interesting. I am also diagnosed with ET (from platelet counts, pathology report noting abnormal platelet morphology, and JAK2 mutation from a marrow biopsy). What made you seek a second opinion?
I had shoulder pain about ten years ago. Had an MRI. Found evidence of a tear. Was told I would need surgery and referred to a sports medicine doctor. He looked at my MRI and said the real problem was my shoulder was frozen, and he could do surgery but the PT after is what would actually be what helped me. Two radiologists and two doctors saw my MRI before this moment. Sure enough, with a little PT I got better.
I’ve had several more medical blunders since then, including a doctor telling me my problem is to lose weight 48 hours before going into emergency surgery.
What I have learned is to be weary of any time I feel like I’m in a “funnel.” Once you’re in the funnel, no one is thinking critically about your issue any more. One person said they found X. Next person reads that and assumes Y and recommends Z. And so on until the alpha is multiplied to hell. Lots of treatments that don’t hurt but don’t help and run up insurance.
I have since used AI the last couple years and it has either concurred with my doctors or given me enough ammo to challenge them. If I were the author, I would trust neither but use Claude to ask how to go back to that clinic and challenge the diagnosis.
Was it 2016 when Geoff hinton said that radiology was a dead career?
Well, we now have the best model of our time (trillions of $$$ of investments) telling us something completely different(and wrong) from a human expert. I would really like someone calling out dario, sam, elon on these things and hear their explanations but alas, a man can only dream.
It’s an odd field, obviously it’s in high demand for diagnostics and anytime you have to do an xray, MRI, etc you have to wait hours for one to become available.
I think they’re artificially stunting the field to raise their wages. For example in my city the medical school only accepts 11 people into the program a year. (With an average graduation rate or 3-5). My niece has been trying for 2 years and finally got in this last year. Even radiology is doing AI assisted diagnostics. Half my MRI’s from this year has Doctor notes and HealthBot (AI) notes attached to them.
~ I’m assuming other schools severely limit their radiology admissions as well. To keep the wages high and the field desirable.
free market solution is just order an x-ray machine from alibaba and setup shop. you could add a credit card swiper + ID + facial recognition to plausibly avoid over-xraying people
These days Xray machines - they don't even suit up in lead or stand behind a wall , just point and shoot. In fact they're nice and portable. I wish i had a xray machine at home.
> Was it 2016 when Geoff hinton said that radiology was a dead career?
Funny how the jobs most at risk of automation now are tech jobs.
yeah that is why you would not use a random llm that is not trained in radiology lmfao
diffusion models are probably a better bet for identifying irregular structures
Always worth a share for this scenario. It's not clear if LLMs are capable of doing actual analysis on medical imaging. For details see this article https://futurism.com/artificial-intelligence/frontier-models...
> As detailed in a new, yet-to-be-peer-reviewed paper, a team of researchers at Stanford University found that frontier AI models readily generated “detailed image descriptions and elaborate reasoning traces, including pathology-biased clinical findings, for images never provided.”
> In other words, the AI models happily came up with answers to questions about a supposedly accompanying image — even if the researchers never even showed it an image.
> As opposed to hallucinations, which involve AI models arbitrarily filling in the gaps within a logical framework, the team coined a new term for the phenomenon: “mirage reasoning.”
> The effect “involves constructing a false epistemic frame, i.e., describing a multi-modal input never provided by the user and basing the rest of the conversation on that, therefore changing the context of the task at hand,” the researchers wrote in their paper.
> The damning findings suggest AI models cheat by diving into the data they were given — and coming up with the rest based on probability, even if it’s almost entirely conjecture.
I work at a telemedicine company. We’ve benchmarked a few frontier LLMs on public medical imaging datasets. One test included high-quality and high-consensus otoscopic images. We didn’t anticipate the models to do well on something so niche, but what concerned us was how poorly calibrated the models were.
I know you can’t trust an LLM’s self-assessed “confidence” of a prediction, but I’ve found that confidence can at least be directionally correct for some tasks. For our benchmarks, however, confidence was poorly correlated. What’s worse is that binary classification models (“Do you see $diagnosis in this photo?”) highly influenced the LLM to confidently predict $diagnosis.
I’m concerned for those using LLMs for diagnostics, and getting confidently led to the wrong conclusion.
But the binary classification models can be made ternary easily. RL on congruence plus penalty for misdiagnosis is easy to set up and gives great results.
What I’ve seen be the true bottleneck is people not setting up the structured data. But making a tiny reasoning model with OPSD -> GRPO is totally doable with a bit of money.
It makes a lot of sense if you understand how these models work but this was a cool read anyways and studies like this are impotent for curbing the unfortunate fever dream some folks seem to be collectively having about LLM omnipotence
I don’t understand how this is a different result than giving any LLM a task that is not completely grounded? I’ve observed this in coding tasks, if I forget to include a file referred to in the spec, the LLM will just hallucinate a version of it and my results suck. If I give it the file (and really, all the information I claimed it had access to), the task works fine. I fixed this in my pipeline with a prompt that does an extensive grounding analysis to determine if the assets I’m giving it are complete with respect to the spec (and that the spec is grounded as well, ie it doesn’t refer to something that is undefined).
I wonder if the above problem can be fixed similarly? Just ask the LLM to do a conservative grounding analysis before jumping to the main task?
It's not different- there's a line of research and reasoning where people who don't use LLM's regularly point out issues that have been known (and more or less solved) for more than a year now (which is an eternity in the LLM space).
Ya, that’s what I guessed. I assume everyone who uses LLMs discovers this on their own eventually if they aren’t made aware of it before it happens.
The absolute only thing that matters is if they are provided an image what's the success rate.
But why should I care? If you demonstrated that a model can perform more accurate diagnoses than a doctor, but also it had this strange behavior when no image was presented, why should that deter me from using the model?
Because you don’t have any way of telling if it actually used the image presented, or based it’s conclusions on a different image it made up
I don't find that persuasive. This is not the error I worry about. Let's say that hypothetically the model just ignores the input image 1 in 10,000 runs. This really doesn't concern me because the output will be trivially detectable incorrect nonsense that doesn't match the symptoms at all. Such a contingency is easily handled by running the image through multiple models and distilling the output, anyway.
The error I worry about is where the model uses the image and comes to an incorrect but symptom matching diagnosis. But in this hypothetical the model is less likely to do so than a doctor, so the choice is either accept the risk of the model or accept a higher risk from a doctor.
Really? You know you could just ask it.
Which would tell you what, exactly? The whole root of the problem is that the model doesn’t “know” either
I put a MRI of my body part into Gemini 2.5 Pro (for fun) and it said it was my brain. These models don't necessarily have that data.
Right now the article reads as "AI can play doctor if you give MRI scans".
If the author would actually go for a second opinion (maybe bring along the AI to let it explain it's findings), then the article could read as "AI did MRI analysis and proved my doctor wrong" (or: "AI did MRI analysis and failed").
Medical opinion will remain one of the last frontiers of LLMs. There so many critical factors that are inappropriate for them. They cannot perform a clinical exam, they have to collect the needed exams and most importantly a life might be at stake (OK, you cannot die from a shoulder problem but you can become handicapped forever).
All that said, as a doctor I am totally open and even happy when a patient refers they took advice from AI. I explain the holes of their reasoning and integrate it with mine. It helps rather than hurts the patient-doctor connection.
Do you think you would ever use LLMs in tandem in your practice to help diagnose and treat?
I often ask, usually Gemini, at a medium abstraction level (not general What the diagnosis is, neither specific like What this high serum Ca means). The answers are correct but not enough and ready for consumption without doctor guidance.
A cardiologist friend goes in deep discussions with a specialised model and he is amazed.
I wish you were my doctor!
I would not use Claude to get a second opinion on anything that’s an image.
I agree with you for some kinds of images, but not all.
LLMs are the best PDF-to-markdown converters, in my experience. I have a CLI that converts PDF to PNG, then run a background agent to "read" each PNG and write it down as markdown; it works flawlessly even for complex math formulas, it can "translate" complex charts, graphs, and tables into words.
It's slow and arguably expensive compared to traditional OCR, but very effective and precise.
Especially an MRI which is a 3D medium —something current LLMs are very bad at.
> MRI which is a 3D medium
The finer detail (which you may already know) is more complicated.
MR does ‘2D’ scans which are a slice, then a gap of non-imaged tissue (typically 10% the slice thickness) then a slice. Each slice is an image with a number of pixels, say 320. Each pixel in the slice is small, eg 0.5mm but very thick due to the slice being thick, which is required for MRI signal. The pixels are 3mm in the shoulder scan done here.
‘3D’ scans don’t have a gap between slices, and are often isotopic, meaning the same resolution in all directions. The voxel (a pixel with depth) would be something like 1mm x 1mm x 1mm.
3D scans are slow, prone to movement artifact and never as pretty in plane as a good 2D. You can reformat them to look ok in any plane.
I know little about radiology, but MRI is a 3D medium. I would not be at all surprised if one could slice an MRI the wrong way to produce a 2D image that fails to show a feature that exists in the source data.
I used it on an ankle fracture xray, it was quite useful to make sense of things. But not like a 2nd opinion.
What's wrong with Claude? I've asked it to analyze images and even Opus 4 would perfect nail it.
Throw a chess board on there. See how it does. It always gets pieces and positions completely wrong because it’s terrible at analyzing images.
Sure, it can see obvious stuff in images, but as far as I'm aware it is not designed for (or tested on) performing the kind microscopic analysis that radiology involves
Claude is the worst FM at image understanding. Prior to gpt-5.4 the only usable models were Gemini and Qwen.
Nice, I have Claude, now I just need an MRI (which in some countries is sort of a hard requirement still unfortunately, long queues)
> It might seem obvious to coders, but the difference between Claude Code and Claude.ai's chat is enormous, even if those two run the same model.
In my experience, Claude Code is vastly better for doing tasks, writing code, etc., but Claude.ai is better for analysis and high-level planning. When I'm working on a new project, I've started using the latter to do the initial planning, get feedback and draw up a spec, which then goes to Claude Code.
For this project, I probably would've done something similar - use CC to get whatever you need out of the image files, but have Claude.ai do the actual review/diagnosing.
Either way, I often think about how far behind most of the world is in really understanding AI. The overwhelming majority of people would never guess that you get vastly different outcomes from the exact same model in a different harness (tbf most people don't know what a harness is). I spend hours every day using AI for a broad range of tasks and still feel like I know a fraction of what there is to know. I haven't even tried the new GLM model (or really any of the open source Chinese ones of the most recent generation). With so many people thinking that the free version of ChatGPT is SOTA AI, a lot of folks are in for a very rude awakening at some point soon.
My only issue with this was the restriction of "Do not look at any data outside of our working folder" is preventing the tool from doing what it does best. I would have given it access to PubMed to pull the latest research on the subject and validate.
I wouldn't consider Claude itself to be the tool that does a job like this, but the tool that pulls in the best data and gives a supported suggestion. And then go through a number of iterations on where it failed to hone in its assessment.
I recently had a pretty bad injury and out of curiosity I asked Gemini what it thought based on some CT scan slice images (and no other information). Surprisingly it came to exactly the same diagnosis and treatment plan as my doctors, but the big advantage is that I could ask it follow up questions any time, whereas the doctors barely explained anything.
This...
Gemini will ask for the specific images it needs to see and show you examples of what each slice will look like.
But unlike the specialists who often come across as abrupt and rush you, Gemini will happily take you on a deep dive and continue to answer all you follow up questions indefinitely.
Getting an actual second opinion seems like the next step?
Radiologists very often have to weigh up different theories, guidelines based on the symptoms. The certainty of their diagnosis is their added value, or if they don’t know they will tell you why.
An AI telling you it could be X or Y because theory ABC… is the academic answer and a luxury clinicians don’t have. AI doesn’t give you what you want. I don’t see any added value in using generic AI models for this
Why wouldn’t you as a doctor by standard run the images through a certified compliant LLM? The actual cost won’t be it and then you can see if you get any new ideas from it. See if it’s just wrong or that it spotted a little detail you missed?
The LLM doesn’t need to be leading or whatever but then you can have a conversation with the patient. If their ChatGPT reports has differences it can be analyzed as well.
It feels like the time constraint of the 15m doctor sessions is the thing. But if prepared immediately after the scan then why not?
There is always time needed to factor in new developments and innovations and that’s fine. Just moving blindly work from human to LLM is wrong. But learning on and testing with all the ai tools incoming constantly won’t be a waste. There will be more and more tools in those processes outside of human judgement, better improve the workflows now to be able to test and plugin new models and systems when they are ready.
> standard run the images through a certified compliant LLM?
Because they don't exist, yet.
In the UK MRIs and other imaging systems need two opinions. there has been a move to allow the first opinion to be ML based.
The _problem_ is that you are basically doing grey smudge analysis, and thats fucking hard.
I've been starting to think of LLM as a great tool for "lead generation," borrowing a term from sales. Most of the things it comes up with don't pan out, but in many cases it's things we wouldn't have thought of, or at least not as quickly. This is especially in the context of web service or SAAS outages.
Because they might bias you. And because you have your own brain, training and experience
> There's something incredibly peaceful about being in the hands of an expert you trust. You don't have to worry anymore and can let them guide you through the process. AI can absolutely shatter that feeling in an uncomfortable way
It's always something along the lines of incredibly peaceful, insanely powerful, extremely interesting, also scary and uncomfortable meanwhile feel like magical super powers and science fiction.
I'm telling you... words have lost meaning.
I tried the same on images of disks in my back. The ai picked a slice not from the middle and used that to say they were too small (since it was looking at a slice towards the edge) and basically told me my life was over and my pain would last forever.
Luckily my disks were fine. Wouldn't trust it. Additionally, an MRI of a pain-free, healthy human still would show lots of things and damage. Unless it coincides with a symptom, it's probably harmless. That's why the history is important when looking at images. Can't just upload something and hope for findings.
Can any LLM give you the rough pixel coordinates of an item it identifies in an image?
I found that while Claude, GPT etc could describe an image, there was no way to link the description back to specific pixels in the image itself. Not even to a bounding box or segment.
I did the same exercise here with medical reports and CT scans for a friend's cancer diagnosis and I got ahead of the oncologists predicting they were about to be cured. Spoilers: yep, cancer free now.
And well, yes, I have the appropriate life science degrees to navigate clinical trial reports and research publications, and that was likely indispensable for steering Claude Code where it went, the radiologist's caution is merited here. But it's just not amateur hour for me to do this, it's 2 decades of academic research in my rearview mirror.
Hey OP my wife had a subscap tear and went through with surgery. Recovery was ROUGH, she couldn’t use that arm at all for almost two months. It’s amazing how much this can cripple a person, we don’t realize how much we use both our hands for our daily lives until one is gone. Even basic stuff like cooking, bathing, etc. If you can avoid surgery you should. Try doing the Buckburger 12 (spelling?) shoulder physiotherapy regiment. You’ll need to even if you get surgery, but this can help with tedonopathy. Also try to identify what is causing the repetitive stress and cut back on that activity.
I do powerlifting and couple years ago, I developed bicep tendinitis on my right arm. Even a tiny bit of weight on it while palm facing up would cause crazy pain. It was funny how I weight from lifting heavy weights to not even being able to carry a plate of food, not being able to press soap dispensers, or give a spot to someone at the gym.
Even a tiny injury can severely cripple us.
Same, sprained my wrist/forearm a while back and couldn't rotate it without pain or take any weight palm up. Couldn't even rotate a door knob.
It wasn't until I pushed through with weights, avoiding any underhand grips or rotation, that it started getting better. Doing bicep curls but keeping the thumb up strengthened the forearm to the point where I was back to the weights I was lifting and could then gradually add some rotation.
As everyone knows here a large language model using probabilistic next phrase approach will not be able to "diagnose" results from an MRI - at least not with enough confidence. It lacks the patient's history and a snapshot along with very varying datapoints in the training set will lead to "approximate" results. Not what a true doctor with a 360 view of a particular patient's health would be able to diagnose. That 'approximation' will get "better" in time - which only means the results will be replaced by yet another approximation.
the more awesome thing to me is that you can run the MRI through an ensemble of LLMs and check to see if they converge among each other
I would like if we could have a site where you submit your MRI then doctor commenters anonymously post their opinion. In general I want a forum where.. when people come with questions for which there are varying opinions we don't just have people leave their 2c and then jet. The thread persists, duplicated ideas get merged, erroneous statements get purged and gradually we refine shining truth
I’m wondering how many radiologist want to work all day, then come home and work.
Many can get paid fee-for-service for after hours work, so would probably prefer that.
I had similar experience, Claude made report of MRI for achilles tear, it measured the gap, but it was completely hallucinated. Achilles tendon is black on the MRI, it instead measured 13mm distance between two completely different things (looked white), the radiologist looked at and saw no gap at all
I wouldn't trust anything from Claude here image-wise (maybe to get a 2nd opinion on the report itself and treatment it's reasonable), but also, on the cases there is something something serious, go to at least 2 different doctors and if they have different opinions go for a 3rd for a decisive vote, besides doing your own research (it's not that uncommon for hard cases to be badly diagnosed).
I have used Gemini 3.1 Pro through CLI to analyze my DICOM images. It gave me the same diagnosis as radiologists. But it was just interesting test
I use LLMs every day and value the benefits they offer, but this approach seems misguided. A smarter way to use them would be to consult the LLM before seeing the specialist and ask it to bring you up to speed on capabilities/limitations and develop a list of important questions to ask.
As a developer I have many times seen Claude's models confidently hallucinate, jump into conclusion. Fable though I used just for 2 days, didn't experience it much in the short-term.
AI use is such a polarizing topic anymore. What ever happened to just waiting and seeing how it all plays out? Since probably none of us is going to be able to predict it anyway.
I'm surprised about the 266 MB of DICOM images, I've never had an MRI but my CT results are generally between 1-2GB (zipped) and I always assumed an MRI would have more data, guess I was wrong about that!
Hey, glad you did that , I have done the exact same think last week but the radiologist interpretation and claudes interpretation was pretty much the same ! you want my doctors number ? lol
German doctors are very prone to quackery. Including their nurses.
I've overheard a nurse at a university hospital argue with patient who used tape on himself about the color of the tape. She was worried he might use the "wrong color". Again: at a university hospital, where they teach MDs. Half of them recommend homeopathic remedies
In short: quaks.
I love how the doctors injected basically water. I imagine the doctor thinking "we did all we could"
You can try sending basic chest radiographs to GPT and it'll fail at interpretation. I'd be wary of premature conclusions.
Its very interesting how people trust LLMs in domains they know little about.
Instead, it is my experiences with LLMs in a domain that I know very well that makes me skeptical of their performance across the board. I find issues in code review multiple times a day with their output, and they are explicitly and extensively trained on this use-case, unlike with the MRI data. Sometimes I veer into other domains I have decent knowledge about (construction, carpentry, landscaping) and LLMs disappoint me there as well.
I suppose Gell-Mann amnesia is a universal human quirk and not restricted to just the news.
I am reminded of the old saying that anyone who diagnoses themselves has a fool for a doctor,
Anecdote on healthcare, adjacent to this.
My dog had been acting off. Wouldn’t eat, was hunched over, looked sad. We took him to a local vet who did an X-ray because they suspected a blockage. They didn’t see one, so they sent us home with standard pain meds.
Randomly, we had a dinner party that night and another vet was there. She heard the story and immediately said, “Go home right now and take your dog to an emergency vet with ultrasound.”
Turns out, at the time, most vets had been trained to use X-rays to look for blockages, but newer evidence showed X-rays were only something like 20% effective compared to ultrasound, which was closer to 95%. (forget percentages but somethign like that)
The ultrasound found an avocado pit stuck in his intestine. He had emergency surgery that night.
That chocolate chunk of an English Lab ended up living until 15, and only needed two more blockage surgeries after that...
I know doctors hate patients reading the internet, and LLMs are going to make that 1000% worse for them. But hopefully over time, we all adapt together and end up better off in the long run.
I used my dog to clean my room.
> They injected me with Traumeel, which is registered in Germany as a homeopathic medicine "without a therapeutic indication".
This single sentence provides a huge clue about what’s going on: This person’s medical team is not good. It’s not hard to get an LLM to perform better than a team that is injecting homeopathic botanical formulations and performing procedures that aren’t indicated for the condition.
I think the real takeaway from this article shouldn’t be “ChatGPT is better than doctors”. It’s a story about LLMs identifying that someone was not in good hands.
> I won't go into the details, but he suggested I get an MRI, which the clinic conveniently had available.
And
> They performed shockwave therapy on my shoulder
(a procedure that may not be effective, but is unlikely to cause any harm)
Its not just about LLM's being better, its about people not trusting DR any more: https://www.physiciansweekly.com/post/the-erosion-of-trust-i...
If we want to fault the article for anything it's that he didnt take that information and go get a 2nd opinion from someone who IS more informed.
Any medical-field-position that recommends homeopathic stuff is instantly in my "full of shit and not trustworthy on anything" list, and I'd go elsewhere immediately and file complaints anywhere I could. There's no excuse at all, they're either fools or scammers, and I want neither anywhere near my (or anyone else's) health.
That said, while I do see homeopathic stuff with that name, it's worth verifying that it isn't just a naming conflict. They're not always unique, particularly across countries, and Traumeel seems to be more of a brand than a specific thing.
I have had terrible experiences with medical professionals. Especially the experienced/senior/specialists. First, they just don't have the time to do a thorough research of my medical history. Second, they are often arrogant and resistant to any kind of critical questions. They have an apparently unwavering belief that they are correct. In fairness, they probably usually are, but they are not infallible, and they are at their weakest when it comes to the edge cases.
AI is completely without ego, and can process all my medical records in minutes. In truth, even today, I would rather have an AI analyse my records.
The thing that annoys me about AI discourse is that AI is a mathematical technique of rapidly increasing efficacy, and yet everyone personifies it. It would help if every time someone said "AI" they supplemented "a mathematical method where extensions onto a very large corpus of information are statistically simulated".
It's not true that "AI makes mistakes" or "ChatGPT is sycophantic". It's just that sometimes the simulated extensions to the training material are accurate, and sometimes they're not.
I think this draws too strong a line between the matrix-math core and the harness that uses it. Those harnesses undoubtedly were built with purpose and the systems fail to achieve that goal. Common usage says the the DMV can make mistakes, like any systems, despite the DMV itself not being a person (and it is common to allege large organizations make mistakes even when no specific individual is making an identifiable mistake). This isn't person-language it's systems/purpose-language.
I understand and somewhat agree with your point, and might have phrased my comment differently. I think my main point is that experts aren't always going to beat "a dynamically simulated extension onto the training material". Often they will, maybe even usually, but sometimes they won't, and I feel like the people in this thread insisting that the experts will always know better are thinking about a competition between experts and a crazy robot instead of a competition between experts and math.
I don't know which country this is but this sure seems like a country where doctors have an incentive to maximize the amount/cost of treatment.
I've seen the two extremes in different countries; either they have a tendency to maximize the complexity of the medical situation, or they minimize it "Don't worry, it's just stress" - I've been to different doctors in different countries and I see a pattern based on the country and the incentive structure. In some countries, they will send you off to do a scan for the slightest malaise.
I don't think it's about quality/coverage of public healthcare (at least not on its own; I have not seen a clear pattern across this axis). I think the difference is to do with the referral system. In countries where you can't go directly to a specialist and need a referral from a General Practitioner/Physician first (I.e. in order to get a refund), you tend to get more false negatives from the GP which block you from going to the next stage "It's nothing, just stress-related." In countries were you have the option to go directly to a specialist, they tend to be much more trigger-happy in terms of giving you a full workup and GPs/Physicians will more easily refer you to a specialist.
And I feel like the attitude extends to the specialists themselves. I suppose making people go to a GP first creates a kind of efficiency and predictability which alleviates pressure to exaggerate the severity of the situation.
Imaging is one area where patients will be able to become more educated to ask better questions.
The areas of premature heavy interventions can be a challenge, especially where there might be room for interpretation and the medical professional didn’t share all possible options.
It’s critical to ask all professionals for all possible options and write each down as they write and explain it. No one’s perfect, and not everyone is negligent or malicious.
> There's something incredibly peaceful about being in the hands of an expert you trust. You don't have to worry anymore and can let them guide you through the process.
> AI can absolutely shatter that feeling in an uncomfortable way ...
I see this as a field report in a time of fundamental transition, from a world without AI, to one that accommodates/incorporates AI. For this to happen, AI will need to become more trustworthy. As for the U.S. medical system, it can't get much worse.
I recently had a similar experience (meaning walking a fence between old and new methods), where I was told I could get an appointment with a human medical practitioner in nine months. So, to resolve my anxiety I consulted AI and got an instant diagnosis, one that was later confirmed by the inaccessible medics.
Being a born skeptic I wasn't going to act on AI's diagnosis, I just wanted to know what was going on, resolve some uncertainty. Another advantage: an AI chatbot doesn't say, "Wait, you're on Medicare? Hmm. See you in nine months."
Don't take this as an endorsement of AI's diagnostic abilities -- it's way too soon for that. In my case it was a slam dunk, about a condition I knew nothing about.
I asked a bird about my father's potential prostate cancer. It gave extremely good advice.
Fucking doctor google bullshit, they want medical treatment but don't want medical advice...
Go with your report back to your doctor.
A family member has cancer and we treat chatgpt as part of the team (our doctor's words). I ingest everything into it, work with it to make a good report. Then at the next visit we review it.
This gives you the best of both worlds. You get peace of mind and the doctor explains why and how the agent was right or wrong.
Twice now we've caught consequential mistakes (wrong pain medication and incorrect notation of the exact mutation that he has). Which have made a difference to his quality of life and treatment path.
Most of what the doctors have said is in line with the agent but when there have been disagreements they've been very reasonable. Sometimes the doctors have gone with the agent's version sometimes they've explained why that's inappropriate.
This could be a starting point for consulting a different human expert for a second opinion (e.g., specific questions to ask about), but I wouldn't put much trust in Claude alone on this.
IME, on an almost daily basis, claude.ai and Claude Code are confidently wrong about something, and use polished language to assert nonsense.[*]
If it's doing that on something easy, like factual knowledge available in text on the Internet, or programming code that can be inspected easily and follows well-known rules, and I can tell, because I understand those things... then there's no way I'm going to assume that Claude doesn't also BS when it comes to someone else's field. Especially not a field that requires some of the smartest people to go a decade of training, just to get started in the field.
[*] And if I confront Claude with its mistakes, eventually it apologizes, and acts as if it's learned something, again mimicking word patterns it's heard real people use and mean, without meaning any of it. I wonder whether the AI user experience would be better, if LLM-ish interfaces weren't implicitly created in the image of fake-it-till-you-make-it overconfident performative sociopathic techbros.
Given the tenor of the comments on this article, I think reading TFA is super important, especially the author's disclaimer at the end, where they state that they're definitely not blindly trusting the AI at this stage, just that they find the differential unsettling.
Went to a new dentist recently and his staff took x-rays of my teeth. I was then waiting for him to come speak with me about what the x-rays show him yet i just took a pic and uploaded it to Gemini. 9 months back my previous dentist said i should have a filling or potentially a crown was needed. I told Gemini this and that ive only about 3 fleeting pain issues in that area. With the x-ray and that info Gemini told me the exact same thing the dentist later came in and told me. If pain comes back and for long periods of time then there's an issue as the x-rays look fine.
Overall i see a great opportunity for x-ray techs (radiographers even when Jensen from NVidia says the first field he recommends not getting into - Radiology which is the step above) to open their own businesses for people who want to use AI for self care and help. Have one doctor or dentist on staff to use as needed.
I would trust a doctor with decades of experience and his diagnosis and treatment plan than some LLM.
It like using WebMD for any ache and pain and it is saying it might either be Lupus or cancer.
If you have 2 clocks you have none.
Or you have an interval?
I have already done that several times, and I found the comments from ChatGTP/Claude, is absolutely bullshit.
I've found ChatGPT to be much better at medicine than doctors. For example, every winter, I would get itchy toes. I was quite concerned because they could be quite painful. But the symptoms were not obvious and they wouldn't occur often. The toes would swell up and become quite red and uncomfortable. One doctor suggested gout, which was not the right diagnosis, because I have no urea problems. Others suggested a skin cream.
No... I told ChatGPT exactly what I told you and it came up with the answer: Chillblains, which should have been obvious given everything I described, yet general practitioners were clueless and often reached for high intervention approaches
Harmless condition fixed by wearing socks. I brought it up with the same GPs who had misdiagnosed me and none had heard of it.
Of course, I'm cognizant that it could be mistaken, but a hospital fed my diabetic aunt a normal sugar diet while she was in a coma and forgot to give her metformin, so I mean, it's not like humans can't be retarded as well. The difference is no one gets offended when I point out ChatGPT has the capacity to be an idiot. Instead they just fix it.
Everyone talking about how doctors know better or have some context that is not shown here.
But are you all forgetting that they literally injected a homeopathic drug on the author?
Between that and Claude sometimes hallucinating, it’s probably worth encouraging patients to take second opinion always.
> But are you all forgetting that they literally injected a homeopathic drug on the author?
I'm no fan of pseudoscience either, but this is where things get blurry. The placebo effect is real even if patients are aware of it. If you give a patient a homeopathic drug while informing them of potential side effects (if any), and then they feel better, have you hurt them? Or have you helped them?
I personally have no interest in trying homeopathic medicines, but the reality is that many patients do take these and are adamant they help. As long as any risks are communicated and there are no serious side effects, it's difficult to make an argument against their use in patients who report a subjective benefit.
> There's something incredibly peaceful about being in the hands of an expert you trust.
I want to know if this is a religious thing, or is related to never having had multiple doctors so bad it seemed like they were actively trying to kill you, or both. I've never had this peaceful experience personally within the realm of healthcare.
> AI can absolutely shatter that feeling in an uncomfortable way
Good. Reality is always good.
> but I don't know if I can fully trust AI either.
WTF??!? Why on earth would anybody ever think they could fully trust LLMs? Even their most vocal proponents concede they aren't infallible panaceas.
Personally my favourite feature of the new ai world is not when I use it directly but it's when one of my managers uses it to try to fix a problem, then issue to me their findings and I have to defend my process to someone who understands neither my process, their suggested solution nor often the problem they're solving in the first place.
It gets worse when they challenge your solutions by feeding it back into the LLM and sending the response on to you, arguing with an LLM is exhausting, arguing by proxy with a human parroting its responses is excruciating.
On the plus side when they do this they can't flood your calendar with those "quick chat" meetings because they know they won't be able to hold a conversation on the issue beyond the first minute.
This happened to me on a paper I submitted recently where it was clear the reviewers used AI. Revising a paper based on LLM review is also exhausting, haha.
I've seen coworkers do this to each other when their expertise is in different domains.
I find that AI can be incredibly useful, but just text dumping its output into a conversation feels insulting.
True, but this was a problem long before AI (read this article, met this guy at a conference who told me x, my boss said blah)
AI probably exacerbates it but crappy managers exist regardless
Before maybe you had to deal with someone hiring schetchy consultants once in a while, but now the managers have a limitless well of dubious answers to draw on at any time.
But now you have a new tool in the upmanagement toolbox: subtlely tell them to implement their idea in prod with Claude Code, and see it for themselves.
Yeah dealing with this now, where my CTO is shipping features that are producing plausible results but just wrong. So, now I gotta spend all day explaining the math behind certain features to her, and she copies and pastes it to Claude.
Sometimes I get a lot of "Do you want me to work up how the UI will look."
They give me what they'd like the UI to look like, but none of the actual content fits outside the one situation they're thinking of.
¯\_(ツ)_/¯
Thankfully where I work now everyone is good about taking no for an answer.
Fight fire with fire. It's over the top passive aggresive, but it works. Whenever I get a JIRA ticket that was clearly AI generated and is 10x too many words, I tell Claude to respond to that ticket with my actual real opinion or suggestion, but make it 10x more words.